Path_Analysis_Across_High_Risk_Individuals.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Path Analysis Across High Risk Individuals.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "KGp_1E3NHWCZ",
        "GP3-VPHgX6b_",
        "6ai_XAA0ZU1r",
        "LjvqsqLOnLUN",
        "SLf182j-qyay",
        "rGCXxCb3t0Mk"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liewmingzuo/sas-network-analysis/blob/main/Path_Analysis_Across_High_Risk_Individuals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ls17sKt0jSpR"
      },
      "source": [
        "#@title Import libraries\n",
        "import swat\n",
        "import lib.python.visualization as viz\n",
        "from IPython.utils import io\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import auc\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import average_precision_score\n",
        "from matplotlib import pyplot as plt\n",
        "from inspect import signature\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYCSchtwcCRd"
      },
      "source": [
        "#@title Connect to CAS single machine server\n",
        "s = swat.CAS('orgrd093.unx.sas.com', 14931)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "b4C7hv1PcwsM",
        "outputId": "338f58e7-43e6-4645-d3f9-facfd511c901"
      },
      "source": [
        "#@title Preload action sets\n",
        "s.loadactionset(actionset=\"decisionTree\")\n",
        "s.loadactionset(actionset=\"fedsql\")\n",
        "s.loadactionset(actionset=\"network\")\n",
        "s.loadactionset(actionset=\"sampling\")\n",
        "s.loadactionset(actionset=\"table\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Added action set 'decisionTree'.\n",
            "NOTE: Added action set 'fedsql'.\n",
            "NOTE: Added action set 'network'.\n",
            "NOTE: Added action set 'sampling'.\n",
            "NOTE: Added action set 'table'.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[actionset]\n",
              "\n",
              " 'table'\n",
              "\n",
              "+ Elapsed: 0.0002s, sys: 0.001s, mem: 0.203mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; actionset</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>table</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0002s</span> &#183; <span class=\"cas-sys\">sys 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.203MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSaiqjC-HGOp"
      },
      "source": [
        "# Part 1: Data Modeling via Projection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "tYd3SVtxddLv",
        "outputId": "12eb3192-00c8-43ca-86a9-442aff96e056"
      },
      "source": [
        "#@title Map a caslib to data directory\n",
        "myCaslib='poc'\n",
        "s.table.addcaslib(\n",
        "    activeOnAdd=False,\n",
        "    caslib=myCaslib,\n",
        "    datasource={'srctype':'path'},\n",
        "    path='/bigdisk/lax/brrees/data/customer/James_Morris/2021jan_poc/'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services added the caslib 'poc'.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[CASLibInfo]\n",
              "\n",
              "   Name  Type Description                                               Path  \\\n",
              " 0  poc  PATH              /bigdisk/lax/brrees/data/customer/James_Morris...   \n",
              " \n",
              "   Definition  Subdirs  Local  Active  Personal  Hidden  Transient  \n",
              " 0                 0.0    1.0     0.0       0.0     0.0        0.0  \n",
              "\n",
              "+ Elapsed: 0.0239s, user: 0.001s, mem: 0.718mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; CASLibInfo</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Type\">Type</th>\n",
              "      <th title=\"Description\">Description</th>\n",
              "      <th title=\"Path\">Path</th>\n",
              "      <th title=\"Definition\">Definition</th>\n",
              "      <th title=\"Subdirs\">Subdirs</th>\n",
              "      <th title=\"Local\">Local</th>\n",
              "      <th title=\"Active\">Active</th>\n",
              "      <th title=\"Personal\">Personal</th>\n",
              "      <th title=\"Hidden\">Hidden</th>\n",
              "      <th title=\"Transient\">Transient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>poc</td>\n",
              "      <td>PATH</td>\n",
              "      <td></td>\n",
              "      <td>/bigdisk/lax/brrees/data/customer/James_Morris...</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0239s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.718MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "iJjWOuUsftlF",
        "outputId": "b060e53f-a654-4e53-a8da-fb430a68f347"
      },
      "source": [
        "#@title Load the entity-resolved nodes data\n",
        "s.table.loadTable(\n",
        "    caslib=myCaslib,\n",
        "    path='nodes.sashdat',\n",
        "    casout={'name':'nodesRaw', 'replace':True}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services made the file nodes.sashdat available as table NODESRAW in caslib CASUSER(brrees).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[caslib]\n",
              "\n",
              " 'CASUSER(brrees)'\n",
              "\n",
              "[tableName]\n",
              "\n",
              " 'NODESRAW'\n",
              "\n",
              "[casTable]\n",
              "\n",
              " CASTable('NODESRAW', caslib='CASUSER(brrees)')\n",
              "\n",
              "+ Elapsed: 0.00215s, user: 0.001s, mem: 0.715mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASUSER(brrees)</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>NODESRAW</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASTable('NODESRAW', caslib='CASUSER(brrees)')</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00215s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 0.715MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "XA3lGC4agpNp",
        "outputId": "a2ae7428-1754-487d-feb8-08b7362c1961"
      },
      "source": [
        "#@title Load the entity-resolved links data\n",
        "s.table.loadTable(\n",
        "    caslib=myCaslib,\n",
        "    path='links.sashdat',\n",
        "    casout={'name':'linksRaw', 'replace':True}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services made the file links.sashdat available as table LINKSRAW in caslib CASUSER(brrees).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[caslib]\n",
              "\n",
              " 'CASUSER(brrees)'\n",
              "\n",
              "[tableName]\n",
              "\n",
              " 'LINKSRAW'\n",
              "\n",
              "[casTable]\n",
              "\n",
              " CASTable('LINKSRAW', caslib='CASUSER(brrees)')\n",
              "\n",
              "+ Elapsed: 0.00199s, user: 0.000999s, mem: 0.713mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASUSER(brrees)</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>LINKSRAW</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASTable('LINKSRAW', caslib='CASUSER(brrees)')</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00199s</span> &#183; <span class=\"cas-user\">user 0.000999s</span> &#183; <span class=\"cas-memory\">mem 0.713MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "cellView": "form",
        "id": "G_MdGBj3gzYF",
        "outputId": "ab536793-0996-4dbb-d957-2ace520a9f0a"
      },
      "source": [
        "#@title Inspect the various node types\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    select node_type, count(*)\n",
        "    from nodesRaw\n",
        "    group by node_type\n",
        "   ;\n",
        "   \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "                 node_type  COUNT\n",
              " 0            ORGANISATION   2232\n",
              " 1               IPADDRESS  10879\n",
              " 2           employee_data   2000\n",
              " 3               TELEPHONE   7953\n",
              " 4                  PERSON  11802\n",
              " 5           customer_data   5000\n",
              " 6                   EMAIL   7950\n",
              " 7                   TAXID   7980\n",
              " 8         employee_access   2000\n",
              " 9         vendor_payments   2000\n",
              " 10                ACCOUNT   9516\n",
              " 11            vendor_data   1000\n",
              " 12                 DEVICE  10862\n",
              " 13                LATLONG   2273\n",
              " 14               LOCATION   9976\n",
              " 15         customer_trans   2000\n",
              " 16  customer_trans_nonmon   1000\n",
              "\n",
              "+ Elapsed: 0.0706s, user: 0.18s, sys: 0.079s, mem: 57.1mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ORGANISATION</td>\n",
              "      <td>2232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IPADDRESS</td>\n",
              "      <td>10879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>employee_data</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TELEPHONE</td>\n",
              "      <td>7953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PERSON</td>\n",
              "      <td>11802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_data</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>EMAIL</td>\n",
              "      <td>7950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>TAXID</td>\n",
              "      <td>7980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>employee_access</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>vendor_payments</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>ACCOUNT</td>\n",
              "      <td>9516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>vendor_data</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>DEVICE</td>\n",
              "      <td>10862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LATLONG</td>\n",
              "      <td>2273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LOCATION</td>\n",
              "      <td>9976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>customer_trans</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>customer_trans_nonmon</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0706s</span> &#183; <span class=\"cas-user\">user 0.18s</span> &#183; <span class=\"cas-sys\">sys 0.079s</span> &#183; <span class=\"cas-memory\">mem 57.1MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "cellView": "form",
        "id": "rsxZZFAlh--d",
        "outputId": "4b4df32c-8a5b-4b9e-ac1c-7ca1edf74012"
      },
      "source": [
        "#@title Inspect the entity-resolved data model\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    create table dataModelLinksRaw{options replace=true} as\n",
        "    select distinct a.link_type, b.node_type as \"from\", c.node_type as \"to\"\n",
        "    from linksRaw as a\n",
        "    join nodesRaw as b\n",
        "    on a.from = b.node\n",
        "    join nodesRaw as c\n",
        "    on a.to = c.node\n",
        "   ;\n",
        "   \"\"\"\n",
        ")\n",
        "\n",
        "viz.graph2dot(\n",
        "    linksDf=s.CASTable('dataModelLinksRaw'),\n",
        "    directed=1,\n",
        "    graphAttrs={'rankdir':'TB', 'ranksep':'3', 'size':'12'},\n",
        "    nodesAttrs={'width':'2', 'height':'1', 'shape':'Mrecord', 'fixedsize': 'true'},\n",
        "    stdout=False\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Table DATAMODELLINKSRAW was created in caslib CASUSER(brrees) with 49 rows returned.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7ff2e0938dd8>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"864pt\" height=\"199pt\"\n viewBox=\"0.00 0.00 864.00 198.56\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(.5366 .5366) rotate(0) translate(4 366)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-366 1606,-366 1606,4 -4,4\"/>\n<!-- vendor_data -->\n<g id=\"node1\" class=\"node\">\n<title>vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M660,-289.5C660,-289.5 780,-289.5 780,-289.5 786,-289.5 792,-295.5 792,-301.5 792,-301.5 792,-349.5 792,-349.5 792,-355.5 786,-361.5 780,-361.5 780,-361.5 660,-361.5 660,-361.5 654,-361.5 648,-355.5 648,-349.5 648,-349.5 648,-301.5 648,-301.5 648,-295.5 654,-289.5 660,-289.5\"/>\n<text text-anchor=\"middle\" x=\"720\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">vendor_data</text>\n</g>\n<!-- PERSON -->\n<g id=\"node2\" class=\"node\">\n<title>PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M498,-.5C498,-.5 618,-.5 618,-.5 624,-.5 630,-6.5 630,-12.5 630,-12.5 630,-60.5 630,-60.5 630,-66.5 624,-72.5 618,-72.5 618,-72.5 498,-72.5 498,-72.5 492,-72.5 486,-66.5 486,-60.5 486,-60.5 486,-12.5 486,-12.5 486,-6.5 492,-.5 498,-.5\"/>\n<text text-anchor=\"middle\" x=\"558\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">PERSON</text>\n</g>\n<!-- vendor_data&#45;&gt;PERSON -->\n<g id=\"edge1\" class=\"edge\">\n<title>vendor_data&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M699.8101,-289.4823C670.2626,-236.7709 615.2736,-138.6732 583.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"586.132,-79.5282 578.1892,-72.5166 580.0259,-82.951 586.132,-79.5282\"/>\n</g>\n<!-- TAXID -->\n<g id=\"node3\" class=\"node\">\n<title>TAXID</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M660,-.5C660,-.5 780,-.5 780,-.5 786,-.5 792,-6.5 792,-12.5 792,-12.5 792,-60.5 792,-60.5 792,-66.5 786,-72.5 780,-72.5 780,-72.5 660,-72.5 660,-72.5 654,-72.5 648,-66.5 648,-60.5 648,-60.5 648,-12.5 648,-12.5 648,-6.5 654,-.5 660,-.5\"/>\n<text text-anchor=\"middle\" x=\"720\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">TAXID</text>\n</g>\n<!-- vendor_data&#45;&gt;TAXID -->\n<g id=\"edge2\" class=\"edge\">\n<title>vendor_data&#45;&gt;TAXID</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M720,-289.4823C720,-237.2047 720,-140.2849 720,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"723.5001,-82.5165 720,-72.5166 716.5001,-82.5166 723.5001,-82.5165\"/>\n</g>\n<!-- LOCATION -->\n<g id=\"node5\" class=\"node\">\n<title>LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M174,-.5C174,-.5 294,-.5 294,-.5 300,-.5 306,-6.5 306,-12.5 306,-12.5 306,-60.5 306,-60.5 306,-66.5 300,-72.5 294,-72.5 294,-72.5 174,-72.5 174,-72.5 168,-72.5 162,-66.5 162,-60.5 162,-60.5 162,-12.5 162,-12.5 162,-6.5 168,-.5 174,-.5\"/>\n<text text-anchor=\"middle\" x=\"234\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">LOCATION</text>\n</g>\n<!-- vendor_data&#45;&gt;LOCATION -->\n<g id=\"edge41\" class=\"edge\">\n<title>vendor_data&#45;&gt;LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M659.4304,-289.4823C568.8726,-235.6321 398.6568,-134.4132 303.277,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"304.9517,-74.6194 294.5677,-72.5166 301.3739,-80.636 304.9517,-74.6194\"/>\n</g>\n<!-- ORGANISATION -->\n<g id=\"node6\" class=\"node\">\n<title>ORGANISATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M12,-.5C12,-.5 132,-.5 132,-.5 138,-.5 144,-6.5 144,-12.5 144,-12.5 144,-60.5 144,-60.5 144,-66.5 138,-72.5 132,-72.5 132,-72.5 12,-72.5 12,-72.5 6,-72.5 0,-66.5 0,-60.5 0,-60.5 0,-12.5 0,-12.5 0,-6.5 6,-.5 12,-.5\"/>\n<text text-anchor=\"middle\" x=\"72\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">ORGANISATION</text>\n</g>\n<!-- vendor_data&#45;&gt;ORGANISATION -->\n<g id=\"edge42\" class=\"edge\">\n<title>vendor_data&#45;&gt;ORGANISATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M647.5848,-292.8463C644.6859,-291.5461 641.8157,-290.2601 639,-289 426.3027,-193.8121 370.3281,-170.2181 153.8439,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"154.9726,-70.0433 144.4152,-69.1537 152.1136,-76.4328 154.9726,-70.0433\"/>\n</g>\n<!-- ACCOUNT -->\n<g id=\"node8\" class=\"node\">\n<title>ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M336,-.5C336,-.5 456,-.5 456,-.5 462,-.5 468,-6.5 468,-12.5 468,-12.5 468,-60.5 468,-60.5 468,-66.5 462,-72.5 456,-72.5 456,-72.5 336,-72.5 336,-72.5 330,-72.5 324,-66.5 324,-60.5 324,-60.5 324,-12.5 324,-12.5 324,-6.5 330,-.5 336,-.5\"/>\n<text text-anchor=\"middle\" x=\"396\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">ACCOUNT</text>\n</g>\n<!-- vendor_data&#45;&gt;ACCOUNT -->\n<g id=\"edge31\" class=\"edge\">\n<title>vendor_data&#45;&gt;ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M679.6203,-289.4823C619.7956,-236.1201 507.8238,-136.2441 443.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"446.1709,-76.5612 436.3784,-72.5166 441.5113,-81.785 446.1709,-76.5612\"/>\n</g>\n<!-- IPADDRESS -->\n<g id=\"node11\" class=\"node\">\n<title>IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1308,-.5C1308,-.5 1428,-.5 1428,-.5 1434,-.5 1440,-6.5 1440,-12.5 1440,-12.5 1440,-60.5 1440,-60.5 1440,-66.5 1434,-72.5 1428,-72.5 1428,-72.5 1308,-72.5 1308,-72.5 1302,-72.5 1296,-66.5 1296,-60.5 1296,-60.5 1296,-12.5 1296,-12.5 1296,-6.5 1302,-.5 1308,-.5\"/>\n<text text-anchor=\"middle\" x=\"1368\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">IPADDRESS</text>\n</g>\n<!-- vendor_data&#45;&gt;IPADDRESS -->\n<g id=\"edge49\" class=\"edge\">\n<title>vendor_data&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M792.4152,-292.8463C795.3141,-291.5461 798.1843,-290.2601 801,-289 1013.6973,-193.8121 1069.6719,-170.2181 1286.1561,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1287.8864,-76.4328 1295.5848,-69.1537 1285.0274,-70.0433 1287.8864,-76.4328\"/>\n</g>\n<!-- TELEPHONE -->\n<g id=\"node13\" class=\"node\">\n<title>TELEPHONE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1470,-.5C1470,-.5 1590,-.5 1590,-.5 1596,-.5 1602,-6.5 1602,-12.5 1602,-12.5 1602,-60.5 1602,-60.5 1602,-66.5 1596,-72.5 1590,-72.5 1590,-72.5 1470,-72.5 1470,-72.5 1464,-72.5 1458,-66.5 1458,-60.5 1458,-60.5 1458,-12.5 1458,-12.5 1458,-6.5 1464,-.5 1470,-.5\"/>\n<text text-anchor=\"middle\" x=\"1530\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">TELEPHONE</text>\n</g>\n<!-- vendor_data&#45;&gt;TELEPHONE -->\n<g id=\"edge43\" class=\"edge\">\n<title>vendor_data&#45;&gt;TELEPHONE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M792.2686,-292.4964C795.2081,-291.2931 798.1268,-290.1229 801,-289 1080.7155,-179.6844 1164.4517,-183.4084 1448.4097,-73.1365\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1449.6849,-76.396 1457.7314,-69.5036 1447.143,-69.8738 1449.6849,-76.396\"/>\n</g>\n<!-- DEVICE -->\n<g id=\"node15\" class=\"node\">\n<title>DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1146,-.5C1146,-.5 1266,-.5 1266,-.5 1272,-.5 1278,-6.5 1278,-12.5 1278,-12.5 1278,-60.5 1278,-60.5 1278,-66.5 1272,-72.5 1266,-72.5 1266,-72.5 1146,-72.5 1146,-72.5 1140,-72.5 1134,-66.5 1134,-60.5 1134,-60.5 1134,-12.5 1134,-12.5 1134,-6.5 1140,-.5 1146,-.5\"/>\n<text text-anchor=\"middle\" x=\"1206\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">DEVICE</text>\n</g>\n<!-- vendor_data&#45;&gt;DEVICE -->\n<g id=\"edge48\" class=\"edge\">\n<title>vendor_data&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M780.5696,-289.4823C871.1274,-235.6321 1041.3432,-134.4132 1136.723,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1138.6261,-80.636 1145.4323,-72.5166 1135.0483,-74.6194 1138.6261,-80.636\"/>\n</g>\n<!-- EMAIL -->\n<g id=\"node16\" class=\"node\">\n<title>EMAIL</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M984,-.5C984,-.5 1104,-.5 1104,-.5 1110,-.5 1116,-6.5 1116,-12.5 1116,-12.5 1116,-60.5 1116,-60.5 1116,-66.5 1110,-72.5 1104,-72.5 1104,-72.5 984,-72.5 984,-72.5 978,-72.5 972,-66.5 972,-60.5 972,-60.5 972,-12.5 972,-12.5 972,-6.5 978,-.5 984,-.5\"/>\n<text text-anchor=\"middle\" x=\"1044\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">EMAIL</text>\n</g>\n<!-- vendor_data&#45;&gt;EMAIL -->\n<g id=\"edge32\" class=\"edge\">\n<title>vendor_data&#45;&gt;EMAIL</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M760.3797,-289.4823C820.2044,-236.1201 932.1762,-136.2441 996.0742,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"998.4887,-81.785 1003.6216,-72.5166 993.8291,-76.5612 998.4887,-81.785\"/>\n</g>\n<!-- LATLONG -->\n<g id=\"node17\" class=\"node\">\n<title>LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M822,-.5C822,-.5 942,-.5 942,-.5 948,-.5 954,-6.5 954,-12.5 954,-12.5 954,-60.5 954,-60.5 954,-66.5 948,-72.5 942,-72.5 942,-72.5 822,-72.5 822,-72.5 816,-72.5 810,-66.5 810,-60.5 810,-60.5 810,-12.5 810,-12.5 810,-6.5 816,-.5 822,-.5\"/>\n<text text-anchor=\"middle\" x=\"882\" y=\"-32.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">LATLONG</text>\n</g>\n<!-- vendor_data&#45;&gt;LATLONG -->\n<g id=\"edge40\" class=\"edge\">\n<title>vendor_data&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M740.1899,-289.4823C769.7374,-236.7709 824.7264,-138.6732 856.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"859.9741,-82.951 861.8108,-72.5166 853.868,-79.5282 859.9741,-82.951\"/>\n</g>\n<!-- vendor_payments -->\n<g id=\"node4\" class=\"node\">\n<title>vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M336,-289.5C336,-289.5 456,-289.5 456,-289.5 462,-289.5 468,-295.5 468,-301.5 468,-301.5 468,-349.5 468,-349.5 468,-355.5 462,-361.5 456,-361.5 456,-361.5 336,-361.5 336,-361.5 330,-361.5 324,-355.5 324,-349.5 324,-349.5 324,-301.5 324,-301.5 324,-295.5 330,-289.5 336,-289.5\"/>\n<text text-anchor=\"middle\" x=\"396\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">vendor_payments</text>\n</g>\n<!-- vendor_payments&#45;&gt;PERSON -->\n<g id=\"edge45\" class=\"edge\">\n<title>vendor_payments&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M416.1899,-289.4823C445.7374,-236.7709 500.7264,-138.6732 532.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"535.9741,-82.951 537.8108,-72.5166 529.868,-79.5282 535.9741,-82.951\"/>\n</g>\n<!-- vendor_payments&#45;&gt;LOCATION -->\n<g id=\"edge3\" class=\"edge\">\n<title>vendor_payments&#45;&gt;LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M375.8101,-289.4823C346.2626,-236.7709 291.2736,-138.6732 259.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"262.132,-79.5282 254.1892,-72.5166 256.0259,-82.951 262.132,-79.5282\"/>\n</g>\n<!-- vendor_payments&#45;&gt;ORGANISATION -->\n<g id=\"edge4\" class=\"edge\">\n<title>vendor_payments&#45;&gt;ORGANISATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M355.6203,-289.4823C295.7956,-236.1201 183.8238,-136.2441 119.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"122.1709,-76.5612 112.3784,-72.5166 117.5113,-81.785 122.1709,-76.5612\"/>\n</g>\n<!-- vendor_payments&#45;&gt;ACCOUNT -->\n<g id=\"edge38\" class=\"edge\">\n<title>vendor_payments&#45;&gt;ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M396,-289.4823C396,-237.2047 396,-140.2849 396,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"399.5001,-82.5165 396,-72.5166 392.5001,-82.5166 399.5001,-82.5165\"/>\n</g>\n<!-- vendor_payments&#45;&gt;LATLONG -->\n<g id=\"edge44\" class=\"edge\">\n<title>vendor_payments&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M456.5696,-289.4823C547.1274,-235.6321 717.3432,-134.4132 812.723,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"814.6261,-80.636 821.4323,-72.5166 811.0483,-74.6194 814.6261,-80.636\"/>\n</g>\n<!-- customer_data -->\n<g id=\"node7\" class=\"node\">\n<title>customer_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M984,-289.5C984,-289.5 1104,-289.5 1104,-289.5 1110,-289.5 1116,-295.5 1116,-301.5 1116,-301.5 1116,-349.5 1116,-349.5 1116,-355.5 1110,-361.5 1104,-361.5 1104,-361.5 984,-361.5 984,-361.5 978,-361.5 972,-355.5 972,-349.5 972,-349.5 972,-301.5 972,-301.5 972,-295.5 978,-289.5 984,-289.5\"/>\n<text text-anchor=\"middle\" x=\"1044\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_data</text>\n</g>\n<!-- customer_data&#45;&gt;PERSON -->\n<g id=\"edge35\" class=\"edge\">\n<title>customer_data&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M983.4304,-289.4823C892.8726,-235.6321 722.6568,-134.4132 627.277,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"628.9517,-74.6194 618.5677,-72.5166 625.3739,-80.636 628.9517,-74.6194\"/>\n</g>\n<!-- customer_data&#45;&gt;TAXID -->\n<g id=\"edge6\" class=\"edge\">\n<title>customer_data&#45;&gt;TAXID</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1003.6203,-289.4823C943.7956,-236.1201 831.8238,-136.2441 767.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"770.1709,-76.5612 760.3784,-72.5166 765.5113,-81.785 770.1709,-76.5612\"/>\n</g>\n<!-- customer_data&#45;&gt;LOCATION -->\n<g id=\"edge34\" class=\"edge\">\n<title>customer_data&#45;&gt;LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M971.7314,-292.4964C968.7919,-291.2931 965.8732,-290.1229 963,-289 683.2845,-179.6844 599.5483,-183.4084 315.5903,-73.1365\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"316.857,-69.8738 306.2686,-69.5036 314.3151,-76.396 316.857,-69.8738\"/>\n</g>\n<!-- customer_data&#45;&gt;ACCOUNT -->\n<g id=\"edge5\" class=\"edge\">\n<title>customer_data&#45;&gt;ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M971.5848,-292.8463C968.6859,-291.5461 965.8157,-290.2601 963,-289 750.3027,-193.8121 694.3281,-170.2181 477.8439,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"478.9726,-70.0433 468.4152,-69.1537 476.1136,-76.4328 478.9726,-70.0433\"/>\n</g>\n<!-- customer_data&#45;&gt;IPADDRESS -->\n<g id=\"edge20\" class=\"edge\">\n<title>customer_data&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1084.3797,-289.4823C1144.2044,-236.1201 1256.1762,-136.2441 1320.0742,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1322.4887,-81.785 1327.6216,-72.5166 1317.8291,-76.5612 1322.4887,-81.785\"/>\n</g>\n<!-- customer_data&#45;&gt;TELEPHONE -->\n<g id=\"edge22\" class=\"edge\">\n<title>customer_data&#45;&gt;TELEPHONE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1104.5696,-289.4823C1195.1274,-235.6321 1365.3432,-134.4132 1460.723,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1462.6261,-80.636 1469.4323,-72.5166 1459.0483,-74.6194 1462.6261,-80.636\"/>\n</g>\n<!-- customer_data&#45;&gt;DEVICE -->\n<g id=\"edge18\" class=\"edge\">\n<title>customer_data&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1064.1899,-289.4823C1093.7374,-236.7709 1148.7264,-138.6732 1180.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1183.9741,-82.951 1185.8108,-72.5166 1177.868,-79.5282 1183.9741,-82.951\"/>\n</g>\n<!-- customer_data&#45;&gt;EMAIL -->\n<g id=\"edge19\" class=\"edge\">\n<title>customer_data&#45;&gt;EMAIL</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1044,-289.4823C1044,-237.2047 1044,-140.2849 1044,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1047.5001,-82.5165 1044,-72.5166 1040.5001,-82.5166 1047.5001,-82.5165\"/>\n</g>\n<!-- customer_data&#45;&gt;LATLONG -->\n<g id=\"edge21\" class=\"edge\">\n<title>customer_data&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1023.8101,-289.4823C994.2626,-236.7709 939.2736,-138.6732 907.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"910.132,-79.5282 902.1892,-72.5166 904.0259,-82.951 910.132,-79.5282\"/>\n</g>\n<!-- customer_trans -->\n<g id=\"node9\" class=\"node\">\n<title>customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M498,-289.5C498,-289.5 618,-289.5 618,-289.5 624,-289.5 630,-295.5 630,-301.5 630,-301.5 630,-349.5 630,-349.5 630,-355.5 624,-361.5 618,-361.5 618,-361.5 498,-361.5 498,-361.5 492,-361.5 486,-355.5 486,-349.5 486,-349.5 486,-301.5 486,-301.5 486,-295.5 492,-289.5 498,-289.5\"/>\n<text text-anchor=\"middle\" x=\"558\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_trans</text>\n</g>\n<!-- customer_trans&#45;&gt;PERSON -->\n<g id=\"edge27\" class=\"edge\">\n<title>customer_trans&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M558,-289.4823C558,-237.2047 558,-140.2849 558,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"561.5001,-82.5165 558,-72.5166 554.5001,-82.5166 561.5001,-82.5165\"/>\n</g>\n<!-- customer_trans&#45;&gt;LOCATION -->\n<g id=\"edge7\" class=\"edge\">\n<title>customer_trans&#45;&gt;LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M517.6203,-289.4823C457.7956,-236.1201 345.8238,-136.2441 281.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"284.1709,-76.5612 274.3784,-72.5166 279.5113,-81.785 284.1709,-76.5612\"/>\n</g>\n<!-- customer_trans&#45;&gt;ORGANISATION -->\n<g id=\"edge8\" class=\"edge\">\n<title>customer_trans&#45;&gt;ORGANISATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M497.4304,-289.4823C406.8726,-235.6321 236.6568,-134.4132 141.277,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"142.9517,-74.6194 132.5677,-72.5166 139.3739,-80.636 142.9517,-74.6194\"/>\n</g>\n<!-- customer_trans&#45;&gt;ACCOUNT -->\n<g id=\"edge24\" class=\"edge\">\n<title>customer_trans&#45;&gt;ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M537.8101,-289.4823C508.2626,-236.7709 453.2736,-138.6732 421.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"424.132,-79.5282 416.1892,-72.5166 418.0259,-82.951 424.132,-79.5282\"/>\n</g>\n<!-- customer_trans&#45;&gt;IPADDRESS -->\n<g id=\"edge25\" class=\"edge\">\n<title>customer_trans&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M630.2686,-292.4964C633.2081,-291.2931 636.1268,-290.1229 639,-289 918.7155,-179.6844 1002.4517,-183.4084 1286.4097,-73.1365\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1287.6849,-76.396 1295.7314,-69.5036 1285.143,-69.8738 1287.6849,-76.396\"/>\n</g>\n<!-- customer_trans&#45;&gt;DEVICE -->\n<g id=\"edge17\" class=\"edge\">\n<title>customer_trans&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M630.4152,-292.8463C633.3141,-291.5461 636.1843,-290.2601 639,-289 851.6973,-193.8121 907.6719,-170.2181 1124.1561,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1125.8864,-76.4328 1133.5848,-69.1537 1123.0274,-70.0433 1125.8864,-76.4328\"/>\n</g>\n<!-- customer_trans&#45;&gt;LATLONG -->\n<g id=\"edge26\" class=\"edge\">\n<title>customer_trans&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M598.3797,-289.4823C658.2044,-236.1201 770.1762,-136.2441 834.0742,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"836.4887,-81.785 841.6216,-72.5166 831.8291,-76.5612 836.4887,-81.785\"/>\n</g>\n<!-- employee_access -->\n<g id=\"node10\" class=\"node\">\n<title>employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1146,-289.5C1146,-289.5 1266,-289.5 1266,-289.5 1272,-289.5 1278,-295.5 1278,-301.5 1278,-301.5 1278,-349.5 1278,-349.5 1278,-355.5 1272,-361.5 1266,-361.5 1266,-361.5 1146,-361.5 1146,-361.5 1140,-361.5 1134,-355.5 1134,-349.5 1134,-349.5 1134,-301.5 1134,-301.5 1134,-295.5 1140,-289.5 1146,-289.5\"/>\n<text text-anchor=\"middle\" x=\"1206\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">employee_access</text>\n</g>\n<!-- employee_access&#45;&gt;PERSON -->\n<g id=\"edge46\" class=\"edge\">\n<title>employee_access&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1133.5848,-292.8463C1130.6859,-291.5461 1127.8157,-290.2601 1125,-289 912.3027,-193.8121 856.3281,-170.2181 639.8439,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"640.9726,-70.0433 630.4152,-69.1537 638.1136,-76.4328 640.9726,-70.0433\"/>\n</g>\n<!-- employee_access&#45;&gt;IPADDRESS -->\n<g id=\"edge9\" class=\"edge\">\n<title>employee_access&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1226.1899,-289.4823C1255.7374,-236.7709 1310.7264,-138.6732 1342.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1345.9741,-82.951 1347.8108,-72.5166 1339.868,-79.5282 1345.9741,-82.951\"/>\n</g>\n<!-- employee_access&#45;&gt;DEVICE -->\n<g id=\"edge37\" class=\"edge\">\n<title>employee_access&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1206,-289.4823C1206,-237.2047 1206,-140.2849 1206,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1209.5001,-82.5165 1206,-72.5166 1202.5001,-82.5166 1209.5001,-82.5165\"/>\n</g>\n<!-- employee_access&#45;&gt;LATLONG -->\n<g id=\"edge28\" class=\"edge\">\n<title>employee_access&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1165.6203,-289.4823C1105.7956,-236.1201 993.8238,-136.2441 929.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"932.1709,-76.5612 922.3784,-72.5166 927.5113,-81.785 932.1709,-76.5612\"/>\n</g>\n<!-- employee_data -->\n<g id=\"node12\" class=\"node\">\n<title>employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M822,-289.5C822,-289.5 942,-289.5 942,-289.5 948,-289.5 954,-295.5 954,-301.5 954,-301.5 954,-349.5 954,-349.5 954,-355.5 948,-361.5 942,-361.5 942,-361.5 822,-361.5 822,-361.5 816,-361.5 810,-355.5 810,-349.5 810,-349.5 810,-301.5 810,-301.5 810,-295.5 816,-289.5 822,-289.5\"/>\n<text text-anchor=\"middle\" x=\"882\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">employee_data</text>\n</g>\n<!-- employee_data&#45;&gt;PERSON -->\n<g id=\"edge13\" class=\"edge\">\n<title>employee_data&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M841.6203,-289.4823C781.7956,-236.1201 669.8238,-136.2441 605.9258,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"608.1709,-76.5612 598.3784,-72.5166 603.5113,-81.785 608.1709,-76.5612\"/>\n</g>\n<!-- employee_data&#45;&gt;TAXID -->\n<g id=\"edge14\" class=\"edge\">\n<title>employee_data&#45;&gt;TAXID</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M861.8101,-289.4823C832.2626,-236.7709 777.2736,-138.6732 745.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"748.132,-79.5282 740.1892,-72.5166 742.0259,-82.951 748.132,-79.5282\"/>\n</g>\n<!-- employee_data&#45;&gt;LOCATION -->\n<g id=\"edge12\" class=\"edge\">\n<title>employee_data&#45;&gt;LOCATION</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M809.5848,-292.8463C806.6859,-291.5461 803.8157,-290.2601 801,-289 588.3027,-193.8121 532.3281,-170.2181 315.8439,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"316.9726,-70.0433 306.4152,-69.1537 314.1136,-76.4328 316.9726,-70.0433\"/>\n</g>\n<!-- employee_data&#45;&gt;ACCOUNT -->\n<g id=\"edge10\" class=\"edge\">\n<title>employee_data&#45;&gt;ACCOUNT</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M821.4304,-289.4823C730.8726,-235.6321 560.6568,-134.4132 465.277,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"466.9517,-74.6194 456.5677,-72.5166 463.3739,-80.636 466.9517,-74.6194\"/>\n</g>\n<!-- employee_data&#45;&gt;IPADDRESS -->\n<g id=\"edge11\" class=\"edge\">\n<title>employee_data&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M942.5696,-289.4823C1033.1274,-235.6321 1203.3432,-134.4132 1298.723,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1300.6261,-80.636 1307.4323,-72.5166 1297.0483,-74.6194 1300.6261,-80.636\"/>\n</g>\n<!-- employee_data&#45;&gt;TELEPHONE -->\n<g id=\"edge15\" class=\"edge\">\n<title>employee_data&#45;&gt;TELEPHONE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M954.4152,-292.8463C957.3141,-291.5461 960.1843,-290.2601 963,-289 1175.6973,-193.8121 1231.6719,-170.2181 1448.1561,-73.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1449.8864,-76.4328 1457.5848,-69.1537 1447.0274,-70.0433 1449.8864,-76.4328\"/>\n</g>\n<!-- employee_data&#45;&gt;DEVICE -->\n<g id=\"edge47\" class=\"edge\">\n<title>employee_data&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M922.3797,-289.4823C982.2044,-236.1201 1094.1762,-136.2441 1158.0742,-79.2487\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1160.4887,-81.785 1165.6216,-72.5166 1155.8291,-76.5612 1160.4887,-81.785\"/>\n</g>\n<!-- employee_data&#45;&gt;EMAIL -->\n<g id=\"edge29\" class=\"edge\">\n<title>employee_data&#45;&gt;EMAIL</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M902.1899,-289.4823C931.7374,-236.7709 986.7264,-138.6732 1018.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1021.9741,-82.951 1023.8108,-72.5166 1015.868,-79.5282 1021.9741,-82.951\"/>\n</g>\n<!-- employee_data&#45;&gt;LATLONG -->\n<g id=\"edge30\" class=\"edge\">\n<title>employee_data&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M882,-289.4823C882,-237.2047 882,-140.2849 882,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"885.5001,-82.5165 882,-72.5166 878.5001,-82.5166 885.5001,-82.5165\"/>\n</g>\n<!-- customer_trans_nonmon -->\n<g id=\"node14\" class=\"node\">\n<title>customer_trans_nonmon</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1308,-289.5C1308,-289.5 1428,-289.5 1428,-289.5 1434,-289.5 1440,-295.5 1440,-301.5 1440,-301.5 1440,-349.5 1440,-349.5 1440,-355.5 1434,-361.5 1428,-361.5 1428,-361.5 1308,-361.5 1308,-361.5 1302,-361.5 1296,-355.5 1296,-349.5 1296,-349.5 1296,-301.5 1296,-301.5 1296,-295.5 1302,-289.5 1308,-289.5\"/>\n<text text-anchor=\"middle\" x=\"1368\" y=\"-321.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_trans_nonmon</text>\n</g>\n<!-- customer_trans_nonmon&#45;&gt;PERSON -->\n<g id=\"edge23\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&gt;PERSON</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1295.7314,-292.4964C1292.7919,-291.2931 1289.8732,-290.1229 1287,-289 1007.2845,-179.6844 923.5483,-183.4084 639.5903,-73.1365\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"640.857,-69.8738 630.2686,-69.5036 638.3151,-76.396 640.857,-69.8738\"/>\n</g>\n<!-- customer_trans_nonmon&#45;&gt;IPADDRESS -->\n<g id=\"edge36\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&gt;IPADDRESS</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1368,-289.4823C1368,-237.2047 1368,-140.2849 1368,-82.7764\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1371.5001,-82.5165 1368,-72.5166 1364.5001,-82.5166 1371.5001,-82.5165\"/>\n</g>\n<!-- customer_trans_nonmon&#45;&gt;TELEPHONE -->\n<g id=\"edge33\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&gt;TELEPHONE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1388.1899,-289.4823C1417.7374,-236.7709 1472.7264,-138.6732 1504.8576,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1507.9741,-82.951 1509.8108,-72.5166 1501.868,-79.5282 1507.9741,-82.951\"/>\n</g>\n<!-- customer_trans_nonmon&#45;&gt;DEVICE -->\n<g id=\"edge16\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&gt;DEVICE</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1347.8101,-289.4823C1318.2626,-236.7709 1263.2736,-138.6732 1231.1424,-81.3528\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1234.132,-79.5282 1226.1892,-72.5166 1228.0259,-82.951 1234.132,-79.5282\"/>\n</g>\n<!-- customer_trans_nonmon&#45;&gt;LATLONG -->\n<g id=\"edge39\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&gt;LATLONG</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1307.4304,-289.4823C1216.8726,-235.6321 1046.6568,-134.4132 951.277,-77.6956\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"952.9517,-74.6194 942.5677,-72.5166 949.3739,-80.636 952.9517,-74.6194\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_dfY3zxlyJS",
        "cellView": "form"
      },
      "source": [
        "#@title Define bipartite projection helper function\n",
        "def project(\n",
        "    nodes,\n",
        "    links,\n",
        "    nodeTypes,\n",
        "    linkType,\n",
        "    outNodes,\n",
        "    outLinks,\n",
        "    appendLinks=''\n",
        "    ):\n",
        "  s.datastep.runCode(\n",
        "      code=f\"\"\"\n",
        "        data nodes_;\n",
        "          set {nodes};\n",
        "          if node_type in ({','.join(\"'{0}'\".format(x) for x in nodeTypes)})\n",
        "          then partitionFlag = 1;\n",
        "          else if node_type in ('{linkType}') \n",
        "          then partitionFlag = 0;\n",
        "          else partitionFlag = .;\n",
        "        run;\n",
        "      \"\"\"\n",
        "  )\n",
        "  s.network.projection(\n",
        "      links=links,\n",
        "      nodes='nodes_',\n",
        "      nodesVar={'vars':['node_type', 'fraud_flag']},\n",
        "      partition='partitionFlag',\n",
        "      outProjectionNodes={'name':outNodes, 'replace':True},\n",
        "      outProjectionLinks={'name':'outLinks_', 'replace':True},\n",
        "      commonNeighbors=True,\n",
        "      cosine=True,\n",
        "      jaccard=True\n",
        "  )\n",
        "  s.datastep.runCode(\n",
        "      code=f\"\"\"\n",
        "        data {outLinks};\n",
        "          set outLinks_(in=newLink) {appendLinks};\n",
        "          length link_type VARCHAR(34);\n",
        "          if newLink then do;\n",
        "              weight = cosine;\n",
        "              link_type = \"SAME \" || \"{linkType}\";\n",
        "          end;\n",
        "          drop cosine;\n",
        "        run;\n",
        "      \"\"\"\n",
        "  )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBDp_PSftP41",
        "outputId": "8dc8151c-8d25-4950-e17e-b1fed05c003b"
      },
      "source": [
        "#@title Build graph based on data model for shortestPath and patternMatch\n",
        "node_types = [\n",
        "              'customer_data',\n",
        "              'employee_data', \n",
        "              'employee_access',\n",
        "              'vendor_payments',\n",
        "              'vendor_data',\n",
        "              'customer_trans',\n",
        "              'customer_trans_nonmon'\n",
        "]\n",
        "link_types = [\n",
        "              'PERSON',\n",
        "              'ACCOUNT',\n",
        "              'ORGANISATION',\n",
        "              'IPADDRESS',\n",
        "              'DEVICE',\n",
        "              'TAXID',\n",
        "              'LATLONG',\n",
        "              'LOCATION',\n",
        "              'EMAIL',\n",
        "              'TELEPHONE'\n",
        "]\n",
        "\n",
        "first=True\n",
        "for link_type in link_types:\n",
        "  if first:\n",
        "    project('nodesRaw', 'linksRaw', node_types, link_type, 'nodesProj', 'linksProj' )\n",
        "    first=False\n",
        "    print('Suppressing output for remainder of loop...')\n",
        "  else:\n",
        "    with io.capture_output() as captured:\n",
        "      project('nodesRaw', 'linksRaw', node_types, link_type, 'nodesProj', 'linksProj', 'linksProj' )\n",
        "print('Done.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 96423.\n",
            "NOTE: The number of links in the input graph is 115523.\n",
            "NOTE: Processing network projection using 16 threads across 1 machines.\n",
            "NOTE: Processing projection used 2.44 (cpu: 27.57) seconds.\n",
            "Suppressing output for remainder of loop...\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "cellView": "form",
        "id": "h2YmtvobxEiP",
        "outputId": "b3b1b239-f942-4c5f-cc10-c46474cd52ea"
      },
      "source": [
        "#@title Inspect the various node types\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    select node_type, count(*)\n",
        "    from nodesProj\n",
        "    group by node_type\n",
        "   ;\n",
        "   \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "                node_type  COUNT\n",
              " 0          employee_data   2000\n",
              " 1          customer_data   5000\n",
              " 2        employee_access   2000\n",
              " 3        vendor_payments   2000\n",
              " 4            vendor_data   1000\n",
              " 5         customer_trans   2000\n",
              " 6  customer_trans_nonmon   1000\n",
              "\n",
              "+ Elapsed: 0.0368s, user: 0.062s, sys: 0.049s, mem: 52.4mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>employee_data</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>employee_access</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vendor_payments</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vendor_data</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_trans</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_trans_nonmon</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0368s</span> &#183; <span class=\"cas-user\">user 0.062s</span> &#183; <span class=\"cas-sys\">sys 0.049s</span> &#183; <span class=\"cas-memory\">mem 52.4MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "cellView": "form",
        "id": "p54svZEV-faA",
        "outputId": "8230fe32-3da6-42f9-e327-7cce688ab564"
      },
      "source": [
        "#@title Inspect the various link types\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    select link_type, count(*)\n",
        "    from linksProj\n",
        "    group by link_type\n",
        "   ;\n",
        "   \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "            link_type   COUNT\n",
              " 0  SAME ORGANISATION   17449\n",
              " 1     SAME TELEPHONE     995\n",
              " 2       SAME ACCOUNT    6023\n",
              " 3        SAME DEVICE  101616\n",
              " 4         SAME EMAIL     203\n",
              " 5         SAME TAXID      35\n",
              " 6        SAME PERSON  118355\n",
              " 7      SAME LOCATION    4653\n",
              " 8     SAME IPADDRESS  102073\n",
              " 9       SAME LATLONG  295301\n",
              "\n",
              "+ Elapsed: 0.502s, user: 0.983s, sys: 0.134s, mem: 92.2mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"link_type\">link_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SAME ORGANISATION</td>\n",
              "      <td>17449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SAME TELEPHONE</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SAME ACCOUNT</td>\n",
              "      <td>6023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SAME DEVICE</td>\n",
              "      <td>101616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SAME EMAIL</td>\n",
              "      <td>203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SAME TAXID</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SAME PERSON</td>\n",
              "      <td>118355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SAME LOCATION</td>\n",
              "      <td>4653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SAME IPADDRESS</td>\n",
              "      <td>102073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SAME LATLONG</td>\n",
              "      <td>295301</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.502s</span> &#183; <span class=\"cas-user\">user 0.983s</span> &#183; <span class=\"cas-sys\">sys 0.134s</span> &#183; <span class=\"cas-memory\">mem 92.2MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "2WuxifILTe-y",
        "outputId": "44ae6239-f928-4dcb-80f9-bf87a099e242"
      },
      "source": [
        "#@title Inspect the projected data model\n",
        "def showProjectedDataModel(nodesProj, linksProj):\n",
        "  s.fedsql.execdirect(\n",
        "      query=f\"\"\"\n",
        "      create table dataModelLinksProj{{options replace=true}} as\n",
        "      select distinct b.node_type as \"from\", c.node_type as \"to\", a.link_type\n",
        "      from {linksProj} as a\n",
        "      join {nodesProj} as b\n",
        "      on a.from = b.node\n",
        "      join {nodesProj} as c\n",
        "      on a.to = c.node\n",
        "    ;\n",
        "    \"\"\"\n",
        "  )\n",
        "\n",
        "  s.datastep.runCode(\n",
        "      code=\"\"\"\n",
        "      data dataModelLinksProj_(keep=from_ to_)\n",
        "          dataModelNodesProj_(keep=node shape fillcolor label);\n",
        "        set dataModelLinksProj;\n",
        "        length link_types $120 label $120 shape fillcolor $16;\n",
        "        retain link_types;\n",
        "        ltype=substr(link_type,6);\n",
        "        by from to link_type;\n",
        "        if FIRST.to then do;\n",
        "          link_types=ltype;\n",
        "        end;\n",
        "        else do;\n",
        "          link_types=CATX('|', link_types, ltype);\n",
        "        end;\n",
        "        if LAST.to then do;\n",
        "          label=CATS('type: {', link_types, '},\\nweight: [0,1]');\n",
        "          from_=from;\n",
        "          to_=CATX('-', from, to);\n",
        "          output dataModelLinksProj_;\n",
        "          from_=to_;\n",
        "          node = to_;\n",
        "          to_=to;\n",
        "          output dataModelLinksProj_;\n",
        "          shape='box';\n",
        "          fillcolor='#E6E6E6';\n",
        "          output dataModelNodesProj_;\n",
        "          node=from;\n",
        "          shape='oval';\n",
        "          fillcolor='white';\n",
        "          label=node;\n",
        "          output dataModelNodesProj_;\n",
        "          node=to;\n",
        "          label=node;\n",
        "          output dataModelNodesProj_;\n",
        "        end;\n",
        "      run;\n",
        "      \"\"\"\n",
        "  )\n",
        "\n",
        "  return viz.graph2dot(\n",
        "      nodesDf=s.CASTable('dataModelNodesProj_'),\n",
        "      linksDf=s.CASTable('dataModelLinksProj_'),\n",
        "      linksFrom='from_',\n",
        "      linksTo='to_',\n",
        "      directed=0,\n",
        "      graphAttrs={'layout':'sfdp', 'overlap':'prism', 'overlap_scaling':'5', 'size':'18', 'splines':'curved'},\n",
        "      nodeAttrs={'label':'label', 'shape':'shape', 'fillcolor':'fillcolor'},\n",
        "      nodesAttrs={'width':'2', 'height':'1', 'style':'filled'},\n",
        "      stdout=False\n",
        "  )\n",
        "\n",
        "showProjectedDataModel('nodesProj', 'linksProj')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Table DATAMODELLINKSPROJ was created in caslib CASUSER(brrees) with 111 rows returned.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Graph at 0x7ff2e02b44e0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"1296pt\" height=\"585pt\"\n viewBox=\"0.00 0.00 1296.00 585.22\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(.4935 .4935) rotate(0) translate(4 1181.838)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-1181.838 2622.0857,-1181.838 2622.0857,4 -4,4\"/>\n<!-- employee_data -->\n<g id=\"node1\" class=\"node\">\n<title>employee_data</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1234.713\" cy=\"-409.152\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1234.713\" y=\"-405.452\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">employee_data</text>\n</g>\n<!-- employee_data&#45;employee_data -->\n<g id=\"node2\" class=\"node\">\n<title>employee_data&#45;employee_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"953.5658,-229.9118 504.5658,-229.9118 504.5658,-157.9118 953.5658,-157.9118 953.5658,-229.9118\"/>\n<text text-anchor=\"middle\" x=\"729.0658\" y=\"-197.7118\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {EMAIL|IPADDRESS|LATLONG|LOCATION|TAXID|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"729.0658\" y=\"-182.7118\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_data&#45;&#45;employee_data&#45;employee_data -->\n<g id=\"edge1\" class=\"edge\">\n<title>employee_data&#45;&#45;employee_data&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1184.657,-383.1805C1101.8247,-344.8381 934.8021,-273.326 827.6297,-229.9582\"/>\n</g>\n<!-- employee_data&#45;vendor_data -->\n<g id=\"node3\" class=\"node\">\n<title>employee_data&#45;vendor_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"934.2135,-500.2787 483.2135,-500.2787 483.2135,-428.2787 934.2135,-428.2787 934.2135,-500.2787\"/>\n<text text-anchor=\"middle\" x=\"708.7135\" y=\"-468.0787\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|EMAIL|LATLONG|LOCATION|PERSON|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"708.7135\" y=\"-453.0787\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_data&#45;&#45;employee_data&#45;vendor_data -->\n<g id=\"edge3\" class=\"edge\">\n<title>employee_data&#45;&#45;employee_data&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1162.6799,-405.9864C940.025,-396.4017 882.7398,-397.0461 796.927,-428.2406\"/>\n</g>\n<!-- employee_data&#45;vendor_payments -->\n<g id=\"node5\" class=\"node\">\n<title>employee_data&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1302.0827,-217.9159 979.0827,-217.9159 979.0827,-145.9159 1302.0827,-145.9159 1302.0827,-217.9159\"/>\n<text text-anchor=\"middle\" x=\"1140.5827\" y=\"-185.7159\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG|LOCATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1140.5827\" y=\"-170.7159\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_data&#45;&#45;employee_data&#45;vendor_payments -->\n<g id=\"edge5\" class=\"edge\">\n<title>employee_data&#45;&#45;employee_data&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1220.1069,-373.8808C1180.4154,-278.0326 1170.1063,-253.1412 1155.5313,-217.9774\"/>\n</g>\n<!-- employee_data&#45;employee_data&#45;&#45;employee_data -->\n<g id=\"edge2\" class=\"edge\">\n<title>employee_data&#45;employee_data&#45;&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M801.1972,-229.9871C901.6927,-275.5413 1081.9398,-352.1026 1175.2,-388.6948\"/>\n</g>\n<!-- vendor_data -->\n<g id=\"node4\" class=\"node\">\n<title>vendor_data</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"626.2516\" cy=\"-584.5703\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"626.2516\" y=\"-580.8703\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">vendor_data</text>\n</g>\n<!-- employee_data&#45;vendor_data&#45;&#45;vendor_data -->\n<g id=\"edge4\" class=\"edge\">\n<title>employee_data&#45;vendor_data&#45;&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M665.6036,-500.4361C646.7708,-517.6712 639.5062,-528.9314 634.0617,-548.4367\"/>\n</g>\n<!-- vendor_data&#45;vendor_data -->\n<g id=\"node23\" class=\"node\">\n<title>vendor_data&#45;vendor_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"546,-681.9874 0,-681.9874 0,-609.9874 546,-609.9874 546,-681.9874\"/>\n<text text-anchor=\"middle\" x=\"273\" y=\"-649.7874\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|EMAIL|LATLONG|LOCATION|ORGANISATION|TAXID|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"273\" y=\"-634.7874\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- vendor_data&#45;&#45;vendor_data&#45;vendor_data -->\n<g id=\"edge31\" class=\"edge\">\n<title>vendor_data&#45;&#45;vendor_data&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M555.0934,-591.3443C522.0455,-596.1993 481.2255,-602.8803 441.0032,-609.9322\"/>\n</g>\n<!-- vendor_data&#45;vendor_payments -->\n<g id=\"node24\" class=\"node\">\n<title>vendor_data&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1149.8459,-420.2302 721.8459,-420.2302 721.8459,-348.2302 1149.8459,-348.2302 1149.8459,-420.2302\"/>\n<text text-anchor=\"middle\" x=\"935.8459\" y=\"-388.0302\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG|LOCATION|ORGANISATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"935.8459\" y=\"-373.0302\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- vendor_data&#45;&#45;vendor_data&#45;vendor_payments -->\n<g id=\"edge33\" class=\"edge\">\n<title>vendor_data&#45;&#45;vendor_data&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M650.9507,-550.5808C700.3562,-484.0048 724.2899,-466.1644 841.3746,-420.2983\"/>\n</g>\n<!-- vendor_payments -->\n<g id=\"node6\" class=\"node\">\n<title>vendor_payments</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1431.4422\" cy=\"-343.629\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1431.4422\" y=\"-339.929\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">vendor_payments</text>\n</g>\n<!-- employee_data&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge6\" class=\"edge\">\n<title>employee_data&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1291.0563,-218.0289C1319.1218,-235.5351 1349.5259,-263.6355 1398.4455,-311.3242\"/>\n</g>\n<!-- vendor_payments&#45;vendor_payments -->\n<g id=\"node22\" class=\"node\">\n<title>vendor_payments&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1640.9888,-72 1212.9888,-72 1212.9888,0 1640.9888,0 1640.9888,-72\"/>\n<text text-anchor=\"middle\" x=\"1426.9888\" y=\"-39.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG|LOCATION|ORGANISATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1426.9888\" y=\"-24.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- vendor_payments&#45;&#45;vendor_payments&#45;vendor_payments -->\n<g id=\"edge29\" class=\"edge\">\n<title>vendor_payments&#45;&#45;vendor_payments&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1435.4465,-307.6725C1437.5538,-248.5112 1435.8612,-131.3092 1432.0482,-72.0723\"/>\n</g>\n<!-- customer_data -->\n<g id=\"node7\" class=\"node\">\n<title>customer_data</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1646.2287\" cy=\"-407.113\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1646.2287\" y=\"-403.413\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_data</text>\n</g>\n<!-- customer_data&#45;customer_data -->\n<g id=\"node8\" class=\"node\">\n<title>customer_data&#45;customer_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"2395.3139,-231.9955 1825.3139,-231.9955 1825.3139,-159.9955 2395.3139,-159.9955 2395.3139,-231.9955\"/>\n<text text-anchor=\"middle\" x=\"2110.3139\" y=\"-199.7955\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|EMAIL|IPADDRESS|LATLONG|LOCATION|TAXID|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"2110.3139\" y=\"-184.7955\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;customer_data -->\n<g id=\"edge7\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;customer_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1704.5752,-385.6235C1790.8585,-349.3779 1952.0357,-276.1946 2043.1317,-232.0243\"/>\n</g>\n<!-- customer_data&#45;customer_trans -->\n<g id=\"node9\" class=\"node\">\n<title>customer_data&#45;customer_trans</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"2048.5919,-714.6934 1797.5919,-714.6934 1797.5919,-642.6934 2048.5919,-642.6934 2048.5919,-714.6934\"/>\n<text text-anchor=\"middle\" x=\"1923.0919\" y=\"-682.4934\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1923.0919\" y=\"-667.4934\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;customer_trans -->\n<g id=\"edge9\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1692.6829,-434.8069C1845.3452,-526.1878 1864.1062,-543.607 1907.5518,-642.6242\"/>\n</g>\n<!-- customer_data&#45;customer_trans_nonmon -->\n<g id=\"node11\" class=\"node\">\n<title>customer_data&#45;customer_trans_nonmon</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"2211.3177,-511.3854 1885.3177,-511.3854 1885.3177,-439.3854 2211.3177,-439.3854 2211.3177,-511.3854\"/>\n<text text-anchor=\"middle\" x=\"2048.3177\" y=\"-479.1854\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|PERSON|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"2048.3177\" y=\"-464.1854\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;customer_trans_nonmon -->\n<g id=\"edge11\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;customer_trans_nonmon</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1718.1903,-410.385C1860.1306,-417.0305 1910.4217,-421.3562 1962.883,-439.2922\"/>\n</g>\n<!-- customer_data&#45;employee_access -->\n<g id=\"node13\" class=\"node\">\n<title>customer_data&#45;employee_access</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1789.3732,-762.1898 1477.3732,-762.1898 1477.3732,-690.1898 1789.3732,-690.1898 1789.3732,-762.1898\"/>\n<text text-anchor=\"middle\" x=\"1633.3732\" y=\"-729.9898\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1633.3732\" y=\"-714.9898\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;employee_access -->\n<g id=\"edge13\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1659.3048,-442.7469C1699.2392,-552.9959 1698.2582,-572.9748 1648.8158,-689.979\"/>\n</g>\n<!-- customer_data&#45;employee_data -->\n<g id=\"node15\" class=\"node\">\n<title>customer_data&#45;employee_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1742.6629,-299.2975 1117.6629,-299.2975 1117.6629,-227.2975 1742.6629,-227.2975 1742.6629,-299.2975\"/>\n<text text-anchor=\"middle\" x=\"1430.1629\" y=\"-267.0975\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|EMAIL|IPADDRESS|LATLONG|LOCATION|PERSON|TAXID|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"1430.1629\" y=\"-252.0975\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;employee_data -->\n<g id=\"edge15\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1623.3234,-372.8615C1596.5798,-333.6482 1579.6802,-313.6032 1552.6386,-299.4237\"/>\n</g>\n<!-- customer_data&#45;vendor_data -->\n<g id=\"node16\" class=\"node\">\n<title>customer_data&#45;vendor_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1448.7287,-525.485 1078.7287,-525.485 1078.7287,-453.485 1448.7287,-453.485 1448.7287,-525.485\"/>\n<text text-anchor=\"middle\" x=\"1263.7287\" y=\"-493.285\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|EMAIL|LATLONG|LOCATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1263.7287\" y=\"-478.285\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;vendor_data -->\n<g id=\"edge17\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1575.0014,-401.3275C1514.0874,-398.8528 1475.3555,-408.9714 1357.6594,-453.4655\"/>\n</g>\n<!-- customer_data&#45;vendor_payments -->\n<g id=\"node17\" class=\"node\">\n<title>customer_data&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1809.1868,-219.1235 1612.1868,-219.1235 1612.1868,-147.1235 1809.1868,-147.1235 1809.1868,-219.1235\"/>\n<text text-anchor=\"middle\" x=\"1710.6868\" y=\"-186.9235\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG},</text>\n<text text-anchor=\"middle\" x=\"1710.6868\" y=\"-171.9235\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;vendor_payments -->\n<g id=\"edge19\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1663.2983,-372.0145C1705.2302,-285.4044 1712.8605,-264.6418 1712.1726,-219.4352\"/>\n</g>\n<!-- customer_data&#45;customer_data&#45;&#45;customer_data -->\n<g id=\"edge8\" class=\"edge\">\n<title>customer_data&#45;customer_data&#45;&#45;customer_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2017.7406,-232.093C1920.4004,-274.1709 1771.0172,-342.5234 1694.7136,-380.2305\"/>\n</g>\n<!-- customer_trans -->\n<g id=\"node10\" class=\"node\">\n<title>customer_trans</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1575.4895\" cy=\"-878.6562\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1575.4895\" y=\"-874.9562\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_trans</text>\n</g>\n<!-- customer_data&#45;customer_trans&#45;&#45;customer_trans -->\n<g id=\"edge10\" class=\"edge\">\n<title>customer_data&#45;customer_trans&#45;&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1895.3128,-714.9622C1828.183,-801.1232 1803.0742,-815.0828 1638.6584,-861.1524\"/>\n</g>\n<!-- customer_trans&#45;customer_trans -->\n<g id=\"node25\" class=\"node\">\n<title>customer_trans&#45;customer_trans</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1975.3469,-1177.838 1418.3469,-1177.838 1418.3469,-1105.838 1975.3469,-1105.838 1975.3469,-1177.838\"/>\n<text text-anchor=\"middle\" x=\"1696.8469\" y=\"-1145.638\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|IPADDRESS|LATLONG|LOCATION|ORGANISATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1696.8469\" y=\"-1130.638\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;customer_trans -->\n<g id=\"edge35\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1586.7258,-914.2186C1606.9212,-964.3924 1648.7995,-1055.3957 1674.8202,-1105.8167\"/>\n</g>\n<!-- customer_trans&#45;employee_access -->\n<g id=\"node26\" class=\"node\">\n<title>customer_trans&#45;employee_access</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1550.6967,-1097.8096 1367.6967,-1097.8096 1367.6967,-1025.8096 1550.6967,-1025.8096 1550.6967,-1097.8096\"/>\n<text text-anchor=\"middle\" x=\"1459.1967\" y=\"-1065.6096\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1459.1967\" y=\"-1050.6096\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;employee_access -->\n<g id=\"edge37\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1565.1449,-914.3405C1545.9138,-979.6949 1536.5355,-1002.1964 1509.1349,-1025.7465\"/>\n</g>\n<!-- customer_trans&#45;employee_data -->\n<g id=\"node27\" class=\"node\">\n<title>customer_trans&#45;employee_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1469.1531,-767.383 1017.1531,-767.383 1017.1531,-695.383 1469.1531,-695.383 1469.1531,-767.383\"/>\n<text text-anchor=\"middle\" x=\"1243.1531\" y=\"-735.183\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|IPADDRESS|LATLONG|LOCATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1243.1531\" y=\"-720.183\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;employee_data -->\n<g id=\"edge39\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1504.133,-873.3696C1439.8603,-865.6974 1406.6069,-846.7003 1294.3429,-767.6191\"/>\n</g>\n<!-- customer_trans&#45;vendor_data -->\n<g id=\"node28\" class=\"node\">\n<title>customer_trans&#45;vendor_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1248.1014,-892.1039 997.1014,-892.1039 997.1014,-820.1039 1248.1014,-820.1039 1248.1014,-892.1039\"/>\n<text text-anchor=\"middle\" x=\"1122.6014\" y=\"-859.9039\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1122.6014\" y=\"-844.9039\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;vendor_data -->\n<g id=\"edge41\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1517.0705,-899.9364C1383.6754,-947.511 1347.7012,-948.2521 1209.1489,-892.2003\"/>\n</g>\n<!-- customer_trans&#45;vendor_payments -->\n<g id=\"node29\" class=\"node\">\n<title>customer_trans&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1736.2668,-635.6937 1539.2668,-635.6937 1539.2668,-563.6937 1736.2668,-563.6937 1736.2668,-635.6937\"/>\n<text text-anchor=\"middle\" x=\"1637.7668\" y=\"-603.4937\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|LATLONG},</text>\n<text text-anchor=\"middle\" x=\"1637.7668\" y=\"-588.4937\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;vendor_payments -->\n<g id=\"edge43\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1599.9633,-844.5203C1652.3063,-770.1468 1655.3242,-749.9654 1642.0766,-635.8898\"/>\n</g>\n<!-- customer_trans_nonmon -->\n<g id=\"node12\" class=\"node\">\n<title>customer_trans_nonmon</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"2136.855\" cy=\"-610.099\" rx=\"80.4049\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"2136.855\" y=\"-606.399\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_trans_nonmon</text>\n</g>\n<!-- customer_data&#45;customer_trans_nonmon&#45;&#45;customer_trans_nonmon -->\n<g id=\"edge12\" class=\"edge\">\n<title>customer_data&#45;customer_trans_nonmon&#45;&#45;customer_trans_nonmon</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2089.8096,-511.614C2115.5308,-535.3517 2123.777,-548.3112 2129.8908,-573.8372\"/>\n</g>\n<!-- customer_trans_nonmon&#45;customer_trans -->\n<g id=\"node30\" class=\"node\">\n<title>customer_trans_nonmon&#45;customer_trans</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1997.9359,-904.0007 1853.9359,-904.0007 1853.9359,-832.0007 1997.9359,-832.0007 1997.9359,-904.0007\"/>\n<text text-anchor=\"middle\" x=\"1925.9359\" y=\"-871.8007\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {PERSON},</text>\n<text text-anchor=\"middle\" x=\"1925.9359\" y=\"-856.8007\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;customer_trans -->\n<g id=\"edge45\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2126.6323,-645.9417C2099.2486,-739.7058 2084.9199,-759.0925 1979.2082,-831.8037\"/>\n</g>\n<!-- customer_trans_nonmon&#45;customer_trans_nonmon -->\n<g id=\"node31\" class=\"node\">\n<title>customer_trans_nonmon&#45;customer_trans_nonmon</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"2618.0857,-702.1799 2225.0857,-702.1799 2225.0857,-630.1799 2618.0857,-630.1799 2618.0857,-702.1799\"/>\n<text text-anchor=\"middle\" x=\"2421.5857\" y=\"-669.9799\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|LATLONG|PERSON|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"2421.5857\" y=\"-654.9799\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;customer_trans_nonmon -->\n<g id=\"edge47\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;customer_trans_nonmon</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2204.6444,-629.442C2211.1127,-630.8889 2217.8671,-632.3669 2224.8255,-633.8598\"/>\n</g>\n<!-- customer_trans_nonmon&#45;employee_access -->\n<g id=\"node32\" class=\"node\">\n<title>customer_trans_nonmon&#45;employee_access</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1826.1149,-897.2314 1682.1149,-897.2314 1682.1149,-825.2314 1826.1149,-825.2314 1826.1149,-897.2314\"/>\n<text text-anchor=\"middle\" x=\"1754.1149\" y=\"-865.0314\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {PERSON},</text>\n<text text-anchor=\"middle\" x=\"1754.1149\" y=\"-850.0314\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;employee_access -->\n<g id=\"edge49\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2112.5384,-644.6964C2040.86,-745.4395 2015.7422,-762.4545 1826.2401,-834.1517\"/>\n</g>\n<!-- customer_trans_nonmon&#45;employee_data -->\n<g id=\"node33\" class=\"node\">\n<title>customer_trans_nonmon&#45;employee_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1787.4792,-524.2014 1461.4792,-524.2014 1461.4792,-452.2014 1787.4792,-452.2014 1787.4792,-524.2014\"/>\n<text text-anchor=\"middle\" x=\"1624.4792\" y=\"-492.0014\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|PERSON|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"1624.4792\" y=\"-477.0014\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;employee_data -->\n<g id=\"edge51\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2079.9518,-584.7116C1999.7749,-550.001 1955.3697,-538.6084 1787.7671,-512.7142\"/>\n</g>\n<!-- customer_trans_nonmon&#45;vendor_payments -->\n<g id=\"node34\" class=\"node\">\n<title>customer_trans_nonmon&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1871.0189,-432.0027 1727.0189,-432.0027 1727.0189,-360.0027 1871.0189,-360.0027 1871.0189,-432.0027\"/>\n<text text-anchor=\"middle\" x=\"1799.0189\" y=\"-399.8027\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {PERSON},</text>\n<text text-anchor=\"middle\" x=\"1799.0189\" y=\"-384.8027\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;vendor_payments -->\n<g id=\"edge53\" class=\"edge\">\n<title>customer_trans_nonmon&#45;&#45;customer_trans_nonmon&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2110.6641,-576.0679C2052.4191,-501.8263 2026.9797,-485.673 1871.2388,-424.2427\"/>\n</g>\n<!-- employee_access -->\n<g id=\"node14\" class=\"node\">\n<title>employee_access</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1394.7575\" cy=\"-881.7045\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1394.7575\" y=\"-878.0045\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">employee_access</text>\n</g>\n<!-- customer_data&#45;employee_access&#45;&#45;employee_access -->\n<g id=\"edge14\" class=\"edge\">\n<title>customer_data&#45;employee_access&#45;&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1608.919,-762.2714C1561.7977,-830.4995 1542.5682,-845.35 1460.0526,-866.2466\"/>\n</g>\n<!-- employee_access&#45;employee_access -->\n<g id=\"node18\" class=\"node\">\n<title>employee_access&#45;employee_access</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1390.2479,-1177.8178 1078.2479,-1177.8178 1078.2479,-1105.8178 1390.2479,-1105.8178 1390.2479,-1177.8178\"/>\n<text text-anchor=\"middle\" x=\"1234.2479\" y=\"-1145.6178\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1234.2479\" y=\"-1130.6178\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_access&#45;&#45;employee_access&#45;employee_access -->\n<g id=\"edge21\" class=\"edge\">\n<title>employee_access&#45;&#45;employee_access&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1368.3162,-915.3278C1335.0232,-964.3658 1279.1132,-1055.0498 1251.0864,-1105.5423\"/>\n</g>\n<!-- employee_access&#45;employee_data -->\n<g id=\"node19\" class=\"node\">\n<title>employee_access&#45;employee_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1008.8958,-700.6192 696.8958,-700.6192 696.8958,-628.6192 1008.8958,-628.6192 1008.8958,-700.6192\"/>\n<text text-anchor=\"middle\" x=\"852.8958\" y=\"-668.4192\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {DEVICE|IPADDRESS|LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"852.8958\" y=\"-653.4192\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_access&#45;&#45;employee_access&#45;employee_data -->\n<g id=\"edge23\" class=\"edge\">\n<title>employee_access&#45;&#45;employee_access&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1323.4134,-876.3512C1060.8273,-856.0996 1030.7602,-843.6252 888.8159,-700.9532\"/>\n</g>\n<!-- employee_access&#45;vendor_data -->\n<g id=\"node20\" class=\"node\">\n<title>employee_access&#45;vendor_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"916.9907,-893.8386 733.9907,-893.8386 733.9907,-821.8386 916.9907,-821.8386 916.9907,-893.8386\"/>\n<text text-anchor=\"middle\" x=\"825.4907\" y=\"-861.6386\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {LATLONG|PERSON},</text>\n<text text-anchor=\"middle\" x=\"825.4907\" y=\"-846.6386\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_access&#45;&#45;employee_access&#45;vendor_data -->\n<g id=\"edge25\" class=\"edge\">\n<title>employee_access&#45;&#45;employee_access&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1328.9536,-896.8763C1083.7337,-953.0305 1038.9481,-956.1561 902.0638,-893.9891\"/>\n</g>\n<!-- employee_access&#45;vendor_payments -->\n<g id=\"node21\" class=\"node\">\n<title>employee_access&#45;vendor_payments</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1281.0314,-632.3073 1137.0314,-632.3073 1137.0314,-560.3073 1281.0314,-560.3073 1281.0314,-632.3073\"/>\n<text text-anchor=\"middle\" x=\"1209.0314\" y=\"-600.1073\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {LATLONG},</text>\n<text text-anchor=\"middle\" x=\"1209.0314\" y=\"-585.1073\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- employee_access&#45;&#45;employee_access&#45;vendor_payments -->\n<g id=\"edge27\" class=\"edge\">\n<title>employee_access&#45;&#45;employee_access&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1356.0396,-851.0816C1300.7716,-805.441 1286.6081,-778.232 1224.468,-632.5677\"/>\n</g>\n<!-- customer_data&#45;employee_data&#45;&#45;employee_data -->\n<g id=\"edge16\" class=\"edge\">\n<title>customer_data&#45;employee_data&#45;&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1336.3827,-299.3745C1316.7268,-313.9005 1296.466,-336.2037 1262.8523,-375.7973\"/>\n</g>\n<!-- customer_data&#45;vendor_data&#45;&#45;vendor_data -->\n<g id=\"edge18\" class=\"edge\">\n<title>customer_data&#45;vendor_data&#45;&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1078.6619,-501.8836C849.2552,-517.9834 798.1367,-526.8559 686.0999,-564.1811\"/>\n</g>\n<!-- customer_data&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge20\" class=\"edge\">\n<title>customer_data&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1613.2688,-219.1709C1581.8566,-235.723 1545.0077,-261.5016 1472.6664,-313.7823\"/>\n</g>\n<!-- employee_access&#45;employee_access&#45;&#45;employee_access -->\n<g id=\"edge22\" class=\"edge\">\n<title>employee_access&#45;employee_access&#45;&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1262.3331,-1105.7665C1296.0519,-1055.8345 1351.186,-966.2905 1378.5483,-916.8448\"/>\n</g>\n<!-- employee_access&#45;employee_data&#45;&#45;employee_data -->\n<g id=\"edge24\" class=\"edge\">\n<title>employee_access&#45;employee_data&#45;&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M878.1385,-628.4047C949.7675,-527.0544 975.1296,-511.2901 1177.9676,-431.4094\"/>\n</g>\n<!-- employee_access&#45;vendor_data&#45;&#45;vendor_data -->\n<g id=\"edge26\" class=\"edge\">\n<title>employee_access&#45;vendor_data&#45;&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M776.4555,-821.6364C670.8066,-742.7627 657.8962,-722.993 634.3162,-620.4967\"/>\n</g>\n<!-- employee_access&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge28\" class=\"edge\">\n<title>employee_access&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1230.9528,-560.2691C1308.8093,-432.5348 1327.832,-405.9193 1384.3959,-370.9323\"/>\n</g>\n<!-- vendor_payments&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge30\" class=\"edge\">\n<title>vendor_payments&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1422.9738,-72.2593C1420.882,-131.5443 1422.5778,-248.529 1426.3881,-307.6388\"/>\n</g>\n<!-- vendor_data&#45;vendor_data&#45;&#45;vendor_data -->\n<g id=\"edge32\" class=\"edge\">\n<title>vendor_data&#45;vendor_data&#45;&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M517.0142,-609.9653C533.1689,-606.9273 548.4389,-603.9552 562.1571,-601.165\"/>\n</g>\n<!-- vendor_data&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge34\" class=\"edge\">\n<title>vendor_data&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1004.0726,-348.041C1127.5912,-284.6145 1166.3961,-284.9202 1365.4901,-328.8864\"/>\n</g>\n<!-- customer_trans&#45;customer_trans&#45;&#45;customer_trans -->\n<g id=\"edge36\" class=\"edge\">\n<title>customer_trans&#45;customer_trans&#45;&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1685.3894,-1105.7263C1664.9158,-1054.9567 1622.6393,-963.1712 1596.7731,-913.2402\"/>\n</g>\n<!-- customer_trans&#45;employee_access&#45;&#45;employee_access -->\n<g id=\"edge38\" class=\"edge\">\n<title>customer_trans&#45;employee_access&#45;&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1445.2214,-1025.8048C1436.3878,-1002.5145 1428.7333,-980.5771 1407.0327,-917.4392\"/>\n</g>\n<!-- customer_trans&#45;employee_data&#45;&#45;employee_data -->\n<g id=\"edge40\" class=\"edge\">\n<title>customer_trans&#45;employee_data&#45;&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1229.6038,-695.0729C1182.0345,-566.6614 1180.6525,-546.2409 1220.5059,-444.7548\"/>\n</g>\n<!-- customer_trans&#45;vendor_data&#45;&#45;vendor_data -->\n<g id=\"edge42\" class=\"edge\">\n<title>customer_trans&#45;vendor_data&#45;&#45;vendor_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M998.6623,-819.9915C782.9441,-755.8673 751.7165,-734.303 654.1701,-618.0905\"/>\n</g>\n<!-- customer_trans&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge44\" class=\"edge\">\n<title>customer_trans&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1627.4169,-563.3835C1595.9455,-454.7953 1582.6948,-436.4609 1477.4773,-371.629\"/>\n</g>\n<!-- customer_trans_nonmon&#45;customer_trans&#45;&#45;customer_trans -->\n<g id=\"edge46\" class=\"edge\">\n<title>customer_trans_nonmon&#45;customer_trans&#45;&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1853.6557,-892.9068C1803.1431,-906.744 1763.3128,-904.2919 1644.9912,-888.2621\"/>\n</g>\n<!-- customer_trans_nonmon&#45;customer_trans_nonmon&#45;&#45;customer_trans_nonmon -->\n<g id=\"edge48\" class=\"edge\">\n<title>customer_trans_nonmon&#45;customer_trans_nonmon&#45;&#45;customer_trans_nonmon</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2273.4355,-630.1279C2253.1533,-626.1765 2233.028,-622.4425 2214.7095,-619.2508\"/>\n</g>\n<!-- customer_trans_nonmon&#45;employee_access&#45;&#45;employee_access -->\n<g id=\"edge50\" class=\"edge\">\n<title>customer_trans_nonmon&#45;employee_access&#45;&#45;employee_access</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1681.915,-894.227C1622.1145,-917.876 1584.2723,-915.8402 1462.7863,-894.1358\"/>\n</g>\n<!-- customer_trans_nonmon&#45;employee_data&#45;&#45;employee_data -->\n<g id=\"edge52\" class=\"edge\">\n<title>customer_trans_nonmon&#45;employee_data&#45;&#45;employee_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1571.8628,-451.9988C1488.4913,-396.6377 1455.3889,-391.6594 1305.9909,-403.2827\"/>\n</g>\n<!-- customer_trans_nonmon&#45;vendor_payments&#45;&#45;vendor_payments -->\n<g id=\"edge54\" class=\"edge\">\n<title>customer_trans_nonmon&#45;vendor_payments&#45;&#45;vendor_payments</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1732.16,-359.9951C1673.5523,-331.9604 1634.5789,-330.1923 1503.1996,-338.727\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2Q7rgoCzK0p",
        "outputId": "ede34276-78ff-4661-f46a-a933e34155f7"
      },
      "source": [
        "#@title Build graph based on data model for centrality\n",
        "node_types_ = [\n",
        "              'customer_data',\n",
        "              'customer_trans',\n",
        "              'employee_access'\n",
        "]\n",
        "link_types_ = [\n",
        "              'PERSON',\n",
        "              'ACCOUNT',\n",
        "              'ORGANISATION',\n",
        "              'IPADDRESS',\n",
        "              'DEVICE',\n",
        "              'TAXID',\n",
        "              'LOCATION',\n",
        "              'EMAIL',\n",
        "              'TELEPHONE'\n",
        "]\n",
        "\n",
        "first=True\n",
        "for link_type in link_types_:\n",
        "  if first:\n",
        "    project('nodesRaw', 'linksRaw', node_types_, link_type, 'nodesProj2_', 'linksProj2_', 'linksRaw' )\n",
        "    first=False\n",
        "    print('Suppressing output for remainder of loop...')\n",
        "  else:\n",
        "    with io.capture_output() as captured:\n",
        "      project('nodesRaw', 'linksRaw', node_types_, link_type, 'nodesProj2_', 'linksProj2_', 'linksProj2_' )\n",
        "print('Done.')\n",
        "\n",
        "s.datastep.runCode(\n",
        "    code=\"\"\"\n",
        "      data linksProj2_;\n",
        "        set linksProj2_;\n",
        "        drop weight;\n",
        "      run;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "node_types = [\n",
        "              'customer_data',\n",
        "              'customer_trans'\n",
        "]\n",
        "\n",
        "link_types = link_types_ + ['employee_access']\n",
        "\n",
        "first=True\n",
        "for link_type in link_types:\n",
        "  if first:\n",
        "    project('nodesRaw', 'linksProj2_', node_types, link_type, 'nodesProj2', 'linksProj2')\n",
        "    first=False\n",
        "    print('Suppressing output for remainder of loop...')\n",
        "  else:\n",
        "    with io.capture_output() as captured:\n",
        "      project('nodesRaw', 'linksProj2_', node_types, link_type, 'nodesProj2', 'linksProj2', 'linksProj2' )\n",
        "print('Done.')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 96423.\n",
            "NOTE: The number of links in the input graph is 115523.\n",
            "NOTE: Processing network projection using 16 threads across 1 machines.\n",
            "NOTE: Processing projection used 1.40 (cpu: 13.32) seconds.\n",
            "Suppressing output for remainder of loop...\n",
            "Done.\n",
            "NOTE: The number of nodes in the input graph is 96423.\n",
            "NOTE: The number of links in the input graph is 422562.\n",
            "NOTE: Processing network projection using 16 threads across 1 machines.\n",
            "NOTE: Processing projection used 1.34 (cpu: 12.26) seconds.\n",
            "Suppressing output for remainder of loop...\n",
            "Done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "cellView": "form",
        "id": "zlFrJ6Av1WUd",
        "outputId": "2cba7b94-516e-4896-c899-d2ca3fcc6c16"
      },
      "source": [
        "#@title Inspect the various node types\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    select node_type, count(*)\n",
        "    from nodesProj2\n",
        "    group by node_type\n",
        "   ;\n",
        "   \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "         node_type  COUNT\n",
              " 0   customer_data   5000\n",
              " 1  customer_trans   2000\n",
              "\n",
              "+ Elapsed: 0.0373s, user: 0.064s, sys: 0.043s, mem: 51.8mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_trans</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0373s</span> &#183; <span class=\"cas-user\">user 0.064s</span> &#183; <span class=\"cas-sys\">sys 0.043s</span> &#183; <span class=\"cas-memory\">mem 51.8MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "cellView": "form",
        "id": "0_X_J6is-Lo5",
        "outputId": "8e2d85b0-7c48-463e-9501-154e068ce2cf"
      },
      "source": [
        "#@title Inspect the various link types\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "    select link_type, count(*)\n",
        "    from linksProj2\n",
        "    group by link_type\n",
        "   ;\n",
        "   \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "               link_type  COUNT\n",
              " 0  SAME employee_access    446\n",
              " 1     SAME ORGANISATION    129\n",
              " 2        SAME TELEPHONE    112\n",
              " 3          SAME ACCOUNT   1305\n",
              " 4           SAME DEVICE     63\n",
              " 5            SAME EMAIL    170\n",
              " 6            SAME TAXID     13\n",
              " 7           SAME PERSON   5038\n",
              " 8         SAME LOCATION     18\n",
              " 9        SAME IPADDRESS     56\n",
              "\n",
              "+ Elapsed: 0.0378s, user: 0.061s, sys: 0.048s, mem: 52.6mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"link_type\">link_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SAME employee_access</td>\n",
              "      <td>446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SAME ORGANISATION</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SAME TELEPHONE</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SAME ACCOUNT</td>\n",
              "      <td>1305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SAME DEVICE</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SAME EMAIL</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SAME TAXID</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SAME PERSON</td>\n",
              "      <td>5038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SAME LOCATION</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SAME IPADDRESS</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0378s</span> &#183; <span class=\"cas-user\">user 0.061s</span> &#183; <span class=\"cas-sys\">sys 0.048s</span> &#183; <span class=\"cas-memory\">mem 52.6MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "cellView": "form",
        "id": "Mw0kSz9z1WUm",
        "outputId": "262722cb-d892-4319-8c3a-b29379cc8775"
      },
      "source": [
        "#@title Inspect the projected data model\n",
        "showProjectedDataModel('nodesProj2', 'linksProj2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Table DATAMODELLINKSPROJ was created in caslib CASUSER(brrees) with 18 rows returned.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Graph at 0x7ff2e02b17f0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"1296pt\" height=\"58pt\"\n viewBox=\"0.00 0.00 1296.00 57.52\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(.719 .719) rotate(0) translate(4 76)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-76 1798.4808,-76 1798.4808,4 -4,4\"/>\n<!-- customer_data -->\n<g id=\"node1\" class=\"node\">\n<title>customer_data</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"684.0594\" cy=\"-36\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"684.0594\" y=\"-32.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_data</text>\n</g>\n<!-- customer_data&#45;customer_data -->\n<g id=\"node2\" class=\"node\">\n<title>customer_data&#45;customer_data</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"601,-72 0,-72 0,0 601,0 601,-72\"/>\n<text text-anchor=\"middle\" x=\"300.5\" y=\"-39.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|EMAIL|employee_access|IPADDRESS|LOCATION|TAXID|TELEPHONE},</text>\n<text text-anchor=\"middle\" x=\"300.5\" y=\"-24.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;customer_data -->\n<g id=\"edge1\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;customer_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M612.8138,-30.6121C609.0225,-30.512 605.1255,-30.4174 601.1345,-30.3283\"/>\n</g>\n<!-- customer_data&#45;customer_trans -->\n<g id=\"node3\" class=\"node\">\n<title>customer_data&#45;customer_trans</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1046.2481,-72 764.2481,-72 764.2481,0 1046.2481,0 1046.2481,-72\"/>\n<text text-anchor=\"middle\" x=\"905.2481\" y=\"-39.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|employee_access|PERSON},</text>\n<text text-anchor=\"middle\" x=\"905.2481\" y=\"-24.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_data&#45;&#45;customer_data&#45;customer_trans -->\n<g id=\"edge3\" class=\"edge\">\n<title>customer_data&#45;&#45;customer_data&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M756.1056,-36C758.6255,-36 761.2302,-36 763.9438,-36\"/>\n</g>\n<!-- customer_data&#45;customer_data&#45;&#45;customer_data -->\n<g id=\"edge2\" class=\"edge\">\n<title>customer_data&#45;customer_data&#45;&#45;customer_data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M601.305,-41.6679C605.1711,-41.5814 608.9487,-41.4897 612.6271,-41.3928\"/>\n</g>\n<!-- customer_trans -->\n<g id=\"node4\" class=\"node\">\n<title>customer_trans</title>\n<ellipse fill=\"#ffffff\" stroke=\"#000000\" cx=\"1126.4618\" cy=\"-36\" rx=\"72\" ry=\"36\"/>\n<text text-anchor=\"middle\" x=\"1126.4618\" y=\"-32.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">customer_trans</text>\n</g>\n<!-- customer_data&#45;customer_trans&#45;&#45;customer_trans -->\n<g id=\"edge4\" class=\"edge\">\n<title>customer_data&#45;customer_trans&#45;&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1046.4114,-36C1049.1542,-36 1051.7856,-36 1054.3304,-36\"/>\n</g>\n<!-- customer_trans&#45;customer_trans -->\n<g id=\"node5\" class=\"node\">\n<title>customer_trans&#45;customer_trans</title>\n<polygon fill=\"#e6e6e6\" stroke=\"#000000\" points=\"1794.4808,-72 1206.4808,-72 1206.4808,0 1794.4808,0 1794.4808,-72\"/>\n<text text-anchor=\"middle\" x=\"1500.4808\" y=\"-39.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">type: {ACCOUNT|DEVICE|employee_access|IPADDRESS|LOCATION|ORGANISATION|PERSON},</text>\n<text text-anchor=\"middle\" x=\"1500.4808\" y=\"-24.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">weight: [0,1]</text>\n</g>\n<!-- customer_trans&#45;&#45;customer_trans&#45;customer_trans -->\n<g id=\"edge5\" class=\"edge\">\n<title>customer_trans&#45;&#45;customer_trans&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1197.6923,-41.4348C1200.5536,-41.5103 1203.4745,-41.5826 1206.45,-41.6516\"/>\n</g>\n<!-- customer_trans&#45;customer_trans&#45;&#45;customer_trans -->\n<g id=\"edge6\" class=\"edge\">\n<title>customer_trans&#45;customer_trans&#45;&#45;customer_trans</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1206.2395,-30.3533C1203.3272,-30.4211 1200.4674,-30.4919 1197.6647,-30.5659\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGp_1E3NHWCZ"
      },
      "source": [
        "# Part 2: Summary and Centrality Based Questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "5xGgJuu6wJyS",
        "outputId": "10e39aa9-38a9-4e97-aa2b-4d462a320304"
      },
      "source": [
        "#@title Q: Describe the total count of nodes and edges in the network you have constructed.\n",
        "r = s.network.summary(\n",
        "    nodes='nodesProj2',\n",
        "    links='linksProj2'\n",
        ")\n",
        "r.ProblemSummary"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 7000.\n",
            "NOTE: The number of links in the input graph is 7350.\n",
            "NOTE: Processing summary metrics.\n",
            "NOTE: Processing summary metrics used 0.00 (cpu: 0.00) seconds.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Problem Summary\n",
              "\n",
              "            Name1           Label1       cValue1  nValue1\n",
              "0        numNodes  Number of Nodes          7000   7000.0\n",
              "1        numLinks  Number of Links          7350   7350.0\n",
              "2  graphDirection  Graph Direction    Undirected      NaN"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Problem Summary</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Name1\">Name1</th>\n",
              "      <th title=\"Label1\">Label1</th>\n",
              "      <th title=\"cValue1\">cValue1</th>\n",
              "      <th title=\"nValue1\">nValue1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>numNodes</td>\n",
              "      <td>Number of Nodes</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>numLinks</td>\n",
              "      <td>Number of Links</td>\n",
              "      <td>7350</td>\n",
              "      <td>7350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>graphDirection</td>\n",
              "      <td>Graph Direction</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "Ey8gL8K15gL3",
        "outputId": "a7778947-5abc-4777-cf35-bec6dd1b0037"
      },
      "source": [
        "#@title Run centrality action\n",
        "s.network.centrality(\n",
        "    nodes='nodesProj2',\n",
        "    links='linksProj2',\n",
        "    outNodes={'name':'nodesCentr', 'replace':True},\n",
        "    nodesVar={'vars':['node_type', 'fraud_flag']},\n",
        "    clusteringCoefficient=True,\n",
        "    degree='both',\n",
        "    between='weight',\n",
        "    close='weight',\n",
        "    pageRank='weight'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 7000.\n",
            "NOTE: The number of links in the input graph is 7350.\n",
            "NOTE: Link weight values for betweenness and closeness centrality are automatically inverted. You can use the AUXWEIGHT= option in the LINKSVAR statement to specify the link weight values used by betweenness and closeness centrality.\n",
            "NOTE: Processing centrality metrics.\n",
            "NOTE: Processing centrality metrics used 0.11 (cpu: 0.90) seconds.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[ProblemSummary]\n",
              "\n",
              " Problem Summary\n",
              " \n",
              "             Name1           Label1       cValue1  nValue1\n",
              " 0        numNodes  Number of Nodes          7000   7000.0\n",
              " 1        numLinks  Number of Links          7350   7350.0\n",
              " 2  graphDirection  Graph Direction    Undirected      NaN\n",
              "\n",
              "[SolutionSummary]\n",
              "\n",
              " Solution Summary\n",
              " \n",
              "           Name1               Label1       cValue1       nValue1\n",
              " 0   problemType         Problem Type    Centrality           NaN\n",
              " 1        status      Solution Status            OK           NaN\n",
              " 2  numTriangles  Number of Triangles         10643  10643.000000\n",
              " 3       cpuTime             CPU Time          0.90      0.900000\n",
              " 4      realTime            Real Time          0.11      0.107495\n",
              "\n",
              "[solutionStatus]\n",
              "\n",
              " 'OK'\n",
              "\n",
              "[solutionStatusCode]\n",
              "\n",
              " 0\n",
              "\n",
              "[numTriangles]\n",
              "\n",
              " 10643\n",
              "\n",
              "[OutputCasTables]\n",
              "\n",
              "             casLib        Name Label  Rows  Columns  \\\n",
              " 0  CASUSER(brrees)  nodesCentr        7000        9   \n",
              " \n",
              "                                            casTable  \n",
              " 0  CASTable('nodesCentr', caslib='CASUSER(brrees)')  \n",
              "\n",
              "+ Elapsed: 0.121s, user: 0.907s, sys: 0.026s, mem: 19.3mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; ProblemSummary</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Problem Summary</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Name1\">Name1</th>\n",
              "      <th title=\"Label1\">Label1</th>\n",
              "      <th title=\"cValue1\">cValue1</th>\n",
              "      <th title=\"nValue1\">nValue1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>numNodes</td>\n",
              "      <td>Number of Nodes</td>\n",
              "      <td>7000</td>\n",
              "      <td>7000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>numLinks</td>\n",
              "      <td>Number of Links</td>\n",
              "      <td>7350</td>\n",
              "      <td>7350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>graphDirection</td>\n",
              "      <td>Graph Direction</td>\n",
              "      <td>Undirected</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; SolutionSummary</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Solution Summary</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Name1\">Name1</th>\n",
              "      <th title=\"Label1\">Label1</th>\n",
              "      <th title=\"cValue1\">cValue1</th>\n",
              "      <th title=\"nValue1\">nValue1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>problemType</td>\n",
              "      <td>Problem Type</td>\n",
              "      <td>Centrality</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>status</td>\n",
              "      <td>Solution Status</td>\n",
              "      <td>OK</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>numTriangles</td>\n",
              "      <td>Number of Triangles</td>\n",
              "      <td>10643</td>\n",
              "      <td>10643.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cpuTime</td>\n",
              "      <td>CPU Time</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>realTime</td>\n",
              "      <td>Real Time</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.107495</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; solutionStatus</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>OK</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; solutionStatusCode</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>0</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; numTriangles</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>10643</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"CAS Library\">casLib</th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Label\">Label</th>\n",
              "      <th title=\"Number of Rows\">Rows</th>\n",
              "      <th title=\"Number of Columns\">Columns</th>\n",
              "      <th title=\"Table\">casTable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CASUSER(brrees)</td>\n",
              "      <td>nodesCentr</td>\n",
              "      <td></td>\n",
              "      <td>7000</td>\n",
              "      <td>9</td>\n",
              "      <td>CASTable('nodesCentr', caslib='CASUSER(brrees)')</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.121s</span> &#183; <span class=\"cas-user\">user 0.907s</span> &#183; <span class=\"cas-sys\">sys 0.026s</span> &#183; <span class=\"cas-memory\">mem 19.3MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "AOYX6cO64qeO",
        "outputId": "f2e7451d-6894-4d54-e17c-d07a88b2101b"
      },
      "source": [
        "#@title Q: How many customers/transactions has at least one other customer/transaction connected within the graph?\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "      select node_type, count(*) as count\n",
        "      from nodesCentr\n",
        "      where centr_degree_out > 0\n",
        "      group by node_type\n",
        "    \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Result Set]\n",
              "\n",
              "         node_type  COUNT\n",
              " 0   customer_data    813\n",
              " 1  customer_trans   2000\n",
              "\n",
              "+ Elapsed: 0.0282s, user: 0.042s, sys: 0.039s, mem: 33.1mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Result Set</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"COUNT\">COUNT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data</td>\n",
              "      <td>813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_trans</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0282s</span> &#183; <span class=\"cas-user\">user 0.042s</span> &#183; <span class=\"cas-sys\">sys 0.039s</span> &#183; <span class=\"cas-memory\">mem 33.1MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "8utL4izb44zD",
        "outputId": "f74a677c-091b-49dc-ddde-2e6b379f5c32"
      },
      "source": [
        "#@title Q: Please provide the top 10 nodes with highest degree centrality score within the graph.\n",
        "s.table.fetch(\n",
        "    table={'name':'nodesCentr'},\n",
        "    fetchVars=['node', 'node_type', 'fraud_flag', 'centr_degree_out_wt'],\n",
        "    sortBy=[{'name':'centr_degree_out_wt', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table NODESCENTR\n",
              " \n",
              "                                                 node      node_type  \\\n",
              " 0  customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...  customer_data   \n",
              " 1  customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...  customer_data   \n",
              " 2  customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...  customer_data   \n",
              " 3  customer_data~282DD724-374B-4294-8AF7-9159015A...  customer_data   \n",
              " 4  customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...  customer_data   \n",
              " 5  customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...  customer_data   \n",
              " 6  customer_data~507369EA-799F-4675-81C4-C7C7D14E...  customer_data   \n",
              " 7  customer_data~3CACC2C9-C906-4257-8EEF-A330E009...  customer_data   \n",
              " 8  customer_data~20388A43-9935-4973-94D4-BC3F18FF...  customer_data   \n",
              " 9  customer_data~EA4B7A84-7A18-4510-B774-EF7B05EB...  customer_data   \n",
              " \n",
              "      fraud_flag centr_degree_out_wt  \n",
              " 0             1        45.818149581  \n",
              " 1             0        42.828427125  \n",
              " 2             1        37.599663291  \n",
              " 3             0         34.41631998  \n",
              " 4             1        34.064129385  \n",
              " 5             1        33.046645015  \n",
              " 6             1        30.535533906  \n",
              " 7             1        29.046645015  \n",
              " 8             1        28.828427125  \n",
              " 9             1        28.599663291  \n",
              "\n",
              "+ Elapsed: 0.00427s, user: 0.004s, mem: 3.72mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table NODESCENTR</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"centr_degree_out_wt\">centr_degree_out_wt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>45.818149581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>42.828427125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>37.599663291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~282DD724-374B-4294-8AF7-9159015A...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>34.41631998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>34.064129385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>33.046645015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_data~507369EA-799F-4675-81C4-C7C7D14E...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>30.535533906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_data~3CACC2C9-C906-4257-8EEF-A330E009...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>29.046645015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_data~20388A43-9935-4973-94D4-BC3F18FF...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>28.828427125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_data~EA4B7A84-7A18-4510-B774-EF7B05EB...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>28.599663291</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00427s</span> &#183; <span class=\"cas-user\">user 0.004s</span> &#183; <span class=\"cas-memory\">mem 3.72MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "QRf0hWlMAQJd",
        "outputId": "d26003bc-ca26-448b-fdb6-005e07f9c9eb"
      },
      "source": [
        "#@title Q: Please provide the top 10 nodes with highest betweenness centrality score within the graph.\n",
        "s.table.fetch(\n",
        "    table={'name':'nodesCentr'},\n",
        "    fetchVars=['node', 'node_type', 'fraud_flag', 'centr_between_wt'],\n",
        "    sortBy=[{'name':'centr_between_wt', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table NODESCENTR\n",
              " \n",
              "                                                 node       node_type  \\\n",
              " 0  customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...  customer_trans   \n",
              " 1  customer_data~1F79FF4F-A803-453A-9780-8AA93344...   customer_data   \n",
              " 2  customer_trans~9FFBB934-3124-48B6-B4C7-4FBA133...  customer_trans   \n",
              " 3  customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...   customer_data   \n",
              " 4  customer_trans~86E98B44-BFCD-4E84-AAF3-13BDD3B...  customer_trans   \n",
              " 5  customer_trans~FF44B22D-0F04-472E-963B-997C5D9...  customer_trans   \n",
              " 6  customer_trans~254B3B60-E989-4935-9A6D-CBD6723...  customer_trans   \n",
              " 7  customer_data~F7B6EB9F-AF36-426E-8610-51B5BE84...   customer_data   \n",
              " 8  customer_data~E2C0BFC4-E950-48AC-8573-9EA44A24...   customer_data   \n",
              " 9  customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...  customer_trans   \n",
              " \n",
              "      fraud_flag centr_between_wt  \n",
              " 0             0     0.0133658118  \n",
              " 1             0     0.0121505137  \n",
              " 2             1     0.0116797337  \n",
              " 3             1     0.0115717475  \n",
              " 4             1     0.0115062376  \n",
              " 5             0      0.010705424  \n",
              " 6             0     0.0099673642  \n",
              " 7             1     0.0091912651  \n",
              " 8             0     0.0088727387  \n",
              " 9             1     0.0086050505  \n",
              "\n",
              "+ Elapsed: 0.00394s, user: 0.003s, mem: 3.72mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table NODESCENTR</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"centr_between_wt\">centr_between_wt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0133658118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~1F79FF4F-A803-453A-9780-8AA93344...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0121505137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_trans~9FFBB934-3124-48B6-B4C7-4FBA133...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0116797337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0115717475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_trans~86E98B44-BFCD-4E84-AAF3-13BDD3B...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0115062376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_trans~FF44B22D-0F04-472E-963B-997C5D9...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>0</td>\n",
              "      <td>0.010705424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_trans~254B3B60-E989-4935-9A6D-CBD6723...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0099673642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_data~F7B6EB9F-AF36-426E-8610-51B5BE84...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0091912651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_data~E2C0BFC4-E950-48AC-8573-9EA44A24...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0088727387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0086050505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00394s</span> &#183; <span class=\"cas-user\">user 0.003s</span> &#183; <span class=\"cas-memory\">mem 3.72MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "bGr39oNJARAU",
        "outputId": "29c4fc08-0a17-4e70-8744-8ecbcfec3c44"
      },
      "source": [
        "#@title Q: Please provide the top 10 nodes with highest closeness centrality score within the graph.\n",
        "s.table.fetch(\n",
        "    table={'name':'nodesCentr'},\n",
        "    fetchVars=['node', 'node_type', 'fraud_flag', 'centr_close_wt'],\n",
        "    sortBy=[{'name':'centr_close_wt', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table NODESCENTR\n",
              " \n",
              "                                                 node       node_type  \\\n",
              " 0  customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...   customer_data   \n",
              " 1  customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...   customer_data   \n",
              " 2  customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...   customer_data   \n",
              " 3  customer_data~1F79FF4F-A803-453A-9780-8AA93344...   customer_data   \n",
              " 4  customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...   customer_data   \n",
              " 5  customer_trans~FF44B22D-0F04-472E-963B-997C5D9...  customer_trans   \n",
              " 6  customer_data~282DD724-374B-4294-8AF7-9159015A...   customer_data   \n",
              " 7  customer_data~3CACC2C9-C906-4257-8EEF-A330E009...   customer_data   \n",
              " 8  customer_data~0660C434-AE0B-40BD-B09A-3EF7E9DF...   customer_data   \n",
              " 9  customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...   customer_data   \n",
              " \n",
              "      fraud_flag centr_close_wt  \n",
              " 0             0   0.0206722165  \n",
              " 1             1    0.020671424  \n",
              " 2             1   0.0206703849  \n",
              " 3             0    0.020663732  \n",
              " 4             1   0.0206615775  \n",
              " 5             0    0.020656314  \n",
              " 6             0    0.020647718  \n",
              " 7             1    0.020641941  \n",
              " 8             0   0.0206415757  \n",
              " 9             1    0.020641454  \n",
              "\n",
              "+ Elapsed: 0.00395s, user: 0.002s, sys: 0.002s, mem: 3.72mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table NODESCENTR</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"centr_close_wt\">centr_close_wt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0206722165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.020671424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0206703849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~1F79FF4F-A803-453A-9780-8AA93344...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.020663732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0206615775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_trans~FF44B22D-0F04-472E-963B-997C5D9...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>0</td>\n",
              "      <td>0.020656314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_data~282DD724-374B-4294-8AF7-9159015A...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.020647718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_data~3CACC2C9-C906-4257-8EEF-A330E009...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.020641941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_data~0660C434-AE0B-40BD-B09A-3EF7E9DF...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0206415757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.020641454</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00395s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-sys\">sys 0.002s</span> &#183; <span class=\"cas-memory\">mem 3.72MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "aNXv7C-pARwc",
        "outputId": "1276f9e6-3f71-41a4-c29d-c7c6b88a06ff"
      },
      "source": [
        "#@title Q: Please provide the top 10 nodes with highest PageRank centrality score within the graph.\n",
        "s.table.fetch(\n",
        "    table={'name':'nodesCentr'},\n",
        "    fetchVars=['node', 'node_type', 'fraud_flag', 'centr_pagerank_wt'],\n",
        "    sortBy=[{'name':'centr_pagerank_wt', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table NODESCENTR\n",
              " \n",
              "                                                 node       node_type  \\\n",
              " 0  customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...   customer_data   \n",
              " 1  customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...   customer_data   \n",
              " 2  customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...   customer_data   \n",
              " 3  customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...   customer_data   \n",
              " 4  customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...   customer_data   \n",
              " 5  customer_data~282DD724-374B-4294-8AF7-9159015A...   customer_data   \n",
              " 6  customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...  customer_trans   \n",
              " 7  customer_data~6121DEC5-C0FB-4EC2-9D12-CDE78D19...   customer_data   \n",
              " 8  customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...   customer_data   \n",
              " 9  customer_data~00B9FF29-992D-45E7-AD13-2BF1B42B...   customer_data   \n",
              " \n",
              "      fraud_flag centr_pagerank_wt  \n",
              " 0             0      0.0011127236  \n",
              " 1             1      0.0010523966  \n",
              " 2             1      0.0008756551  \n",
              " 3             1      0.0008640674  \n",
              " 4             0      0.0008528816  \n",
              " 5             0      0.0008374957  \n",
              " 6             0      0.0007972748  \n",
              " 7             1      0.0007757926  \n",
              " 8             1      0.0007640208  \n",
              " 9             0      0.0007130915  \n",
              "\n",
              "+ Elapsed: 0.0047s, user: 0.004s, mem: 3.72mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table NODESCENTR</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"centr_pagerank_wt\">centr_pagerank_wt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0011127236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0010523966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_data~4A2A7D1C-8EB9-4B78-8825-430A136D...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0008756551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~37E7C16C-4B0F-4B41-B640-FE56EC01...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0008640674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0008528816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_data~282DD724-374B-4294-8AF7-9159015A...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0008374957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0007972748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_data~6121DEC5-C0FB-4EC2-9D12-CDE78D19...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0007757926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_data~5580B0DC-1B29-4886-8CCB-A8A0AA11...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0007640208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_data~00B9FF29-992D-45E7-AD13-2BF1B42B...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0007130915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0047s</span> &#183; <span class=\"cas-user\">user 0.004s</span> &#183; <span class=\"cas-memory\">mem 3.72MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "9mg-kTPYASDL",
        "outputId": "31a07b19-009b-4821-d0fa-99fa979bc8f2"
      },
      "source": [
        "#@title Q: Please provide the top 10 known high risk nodes ordered descending by the size of the linkage neighborhood. Provide the corresponding clustering coefficients to support.\n",
        "s.table.fetch(\n",
        "    table={'name':'nodesCentr', 'where':'fraud_flag EQ 1'},\n",
        "    fetchVars=['node', 'node_type', 'fraud_flag', 'centr_cluster'],\n",
        "    sortBy=[{'name':'centr_cluster', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table NODESCENTR\n",
              " \n",
              "                                                 node       node_type  \\\n",
              " 0  customer_data~AE93BFF1-949A-4A13-8680-67C5F939...   customer_data   \n",
              " 1  customer_data~F2ECF25E-8A8E-477F-9947-8DE8E4B1...   customer_data   \n",
              " 2  customer_data~923CD977-C14B-4D0B-935D-EF1F5A85...   customer_data   \n",
              " 3  customer_data~13BD85F3-E5DD-4E64-B193-B175B1CA...   customer_data   \n",
              " 4  customer_data~20388A43-9935-4973-94D4-BC3F18FF...   customer_data   \n",
              " 5  customer_data~3CACC2C9-C906-4257-8EEF-A330E009...   customer_data   \n",
              " 6  customer_data~507369EA-799F-4675-81C4-C7C7D14E...   customer_data   \n",
              " 7  customer_trans~327D921B-6C31-4E14-BDAD-2BFF585...  customer_trans   \n",
              " 8  customer_trans~E763AA6D-D8C5-4DA5-8CD4-456BAAB...  customer_trans   \n",
              " 9  customer_trans~5B3D7ADF-BEAB-47DD-83C3-F5FAD05...  customer_trans   \n",
              " \n",
              "      fraud_flag centr_cluster  \n",
              " 0             1             1  \n",
              " 1             1             1  \n",
              " 2             1             1  \n",
              " 3             1  0.7777777778  \n",
              " 4             1  0.7292307692  \n",
              " 5             1   0.698005698  \n",
              " 6             1  0.6866096866  \n",
              " 7             1  0.6666666667  \n",
              " 8             1  0.6666666667  \n",
              " 9             1  0.6666666667  \n",
              "\n",
              "+ Elapsed: 0.00192s, user: 0.001s, mem: 3.99mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table NODESCENTR</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"node_type\">node_type</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"centr_cluster\">centr_cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data~AE93BFF1-949A-4A13-8680-67C5F939...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~F2ECF25E-8A8E-477F-9947-8DE8E4B1...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_data~923CD977-C14B-4D0B-935D-EF1F5A85...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~13BD85F3-E5DD-4E64-B193-B175B1CA...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7777777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_data~20388A43-9935-4973-94D4-BC3F18FF...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7292307692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_data~3CACC2C9-C906-4257-8EEF-A330E009...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.698005698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_data~507369EA-799F-4675-81C4-C7C7D14E...</td>\n",
              "      <td>customer_data</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6866096866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_trans~327D921B-6C31-4E14-BDAD-2BFF585...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6666666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_trans~E763AA6D-D8C5-4DA5-8CD4-456BAAB...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6666666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_trans~5B3D7ADF-BEAB-47DD-83C3-F5FAD05...</td>\n",
              "      <td>customer_trans</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6666666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00192s</span> &#183; <span class=\"cas-user\">user 0.001s</span> &#183; <span class=\"cas-memory\">mem 3.99MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "2moPoIypCoQQ",
        "outputId": "5fdd88a4-9a33-429a-bc9a-d5f56d9189a0"
      },
      "source": [
        "#@title Save the projected tables\n",
        "s.table.save(\n",
        "    caslib=myCaslib,\n",
        "    name='linksProj.sashdat',\n",
        "    table='linksProj',\n",
        "    replace=True\n",
        ")\n",
        "s.table.save(\n",
        "    caslib=myCaslib,\n",
        "    name='nodesProj.sashdat',\n",
        "    table='nodesProj',\n",
        "    replace=True\n",
        ")\n",
        "s.table.save(\n",
        "    caslib=myCaslib,\n",
        "    name='linksProj2.sashdat',\n",
        "    table='linksProj2',\n",
        "    replace=True\n",
        ")\n",
        "s.table.save(\n",
        "    caslib=myCaslib,\n",
        "    name='nodesProj2.sashdat',\n",
        "    table='nodesProj2',\n",
        "    replace=True\n",
        ");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services saved the file linksProj.sashdat in caslib poc.\n",
            "NOTE: Cloud Analytic Services saved the file nodesProj.sashdat in caslib poc.\n",
            "NOTE: Cloud Analytic Services saved the file linksProj2.sashdat in caslib poc.\n",
            "NOTE: Cloud Analytic Services saved the file nodesProj2.sashdat in caslib poc.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC-p1R8kIIIC"
      },
      "source": [
        "# Part 3: ShortestPath Based Questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "vBw2Mz9yIQiy"
      },
      "source": [
        "#@title Connect to CAS multiple machine server\n",
        "cluster = swat.CAS('rdcgrd114.unx.sas.com', 14931, nworkers=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "cellView": "form",
        "id": "s1-y7qZ7OAFt",
        "outputId": "41eff89c-0d5f-48d8-8b01-f7709a21360a"
      },
      "source": [
        "#@title Preload Action Sets\n",
        "cluster.loadactionset(actionset=\"fedsql\")\n",
        "cluster.loadactionset(actionset=\"network\")\n",
        "cluster.loadactionset(actionset=\"table\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Added action set 'fedsql'.\n",
            "NOTE: Added action set 'network'.\n",
            "NOTE: Added action set 'table'.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[actionset]\n",
              "\n",
              " 'table'\n",
              "\n",
              "+ Elapsed: 0.0315s, user: 0.0975s, sys: 0.0873s, mem: 20.8mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; actionset</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>table</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0315s</span> &#183; <span class=\"cas-user\">user 0.0975s</span> &#183; <span class=\"cas-sys\">sys 0.0873s</span> &#183; <span class=\"cas-memory\">mem 20.8MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "cellView": "form",
        "id": "uO17uDgtOdVi",
        "outputId": "9a942722-abfc-4808-e739-6bce01ef12c1"
      },
      "source": [
        "#@title Map a caslib to data directory\n",
        "cluster.table.addcaslib(\n",
        "    activeOnAdd=False,\n",
        "    caslib=myCaslib,\n",
        "    datasource={'srctype':'path'},\n",
        "    path='/bigdisk/lax/brrees/data/customer/James_Morris/2021jan_poc/'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services added the caslib 'poc'.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[CASLibInfo]\n",
              "\n",
              "   Name  Type Description                                               Path  \\\n",
              " 0  poc  PATH              /bigdisk/lax/brrees/data/customer/James_Morris...   \n",
              " \n",
              "   Definition  Subdirs  Local  Active  Personal  Hidden  Transient  \n",
              " 0                 0.0    1.0     0.0       0.0     0.0        0.0  \n",
              "\n",
              "+ Elapsed: 0.067s, user: 0.148s, sys: 0.159s, mem: 33.7mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; CASLibInfo</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Type\">Type</th>\n",
              "      <th title=\"Description\">Description</th>\n",
              "      <th title=\"Path\">Path</th>\n",
              "      <th title=\"Definition\">Definition</th>\n",
              "      <th title=\"Subdirs\">Subdirs</th>\n",
              "      <th title=\"Local\">Local</th>\n",
              "      <th title=\"Active\">Active</th>\n",
              "      <th title=\"Personal\">Personal</th>\n",
              "      <th title=\"Hidden\">Hidden</th>\n",
              "      <th title=\"Transient\">Transient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>poc</td>\n",
              "      <td>PATH</td>\n",
              "      <td></td>\n",
              "      <td>/bigdisk/lax/brrees/data/customer/James_Morris...</td>\n",
              "      <td></td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.067s</span> &#183; <span class=\"cas-user\">user 0.148s</span> &#183; <span class=\"cas-sys\">sys 0.159s</span> &#183; <span class=\"cas-memory\">mem 33.7MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "cellView": "form",
        "id": "R3kDMHukOsWe",
        "outputId": "cd19b1cd-c05e-4a37-cca1-ff0ea7bbe67c"
      },
      "source": [
        "#@title Load the projected nodes data\n",
        "cluster.table.loadTable(\n",
        "    caslib=myCaslib,\n",
        "    path='nodesProj.sashdat',\n",
        "    casout={'name':'nodesProj', 'replace':True}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services made the file nodesProj.sashdat available as table NODESPROJ in caslib CASUSERHDFS(brrees).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[caslib]\n",
              "\n",
              " 'CASUSERHDFS(brrees)'\n",
              "\n",
              "[tableName]\n",
              "\n",
              " 'NODESPROJ'\n",
              "\n",
              "[casTable]\n",
              "\n",
              " CASTable('NODESPROJ', caslib='CASUSERHDFS(brrees)')\n",
              "\n",
              "+ Elapsed: 0.207s, user: 0.375s, sys: 0.657s, mem: 207mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASUSERHDFS(brrees)</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>NODESPROJ</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASTable('NODESPROJ', caslib='CASUSERHDFS(brrees)')</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.207s</span> &#183; <span class=\"cas-user\">user 0.375s</span> &#183; <span class=\"cas-sys\">sys 0.657s</span> &#183; <span class=\"cas-memory\">mem 207MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "cellView": "form",
        "id": "BYcZxg80OsWj",
        "outputId": "04eca6c7-6ef0-4502-c38e-f0d95a874da1"
      },
      "source": [
        "#@title Load the projected links data\n",
        "cluster.table.loadTable(\n",
        "    caslib=myCaslib,\n",
        "    path='linksProj.sashdat',\n",
        "    casout={'name':'linksProj', 'replace':True}\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Cloud Analytic Services made the file linksProj.sashdat available as table LINKSPROJ in caslib CASUSERHDFS(brrees).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[caslib]\n",
              "\n",
              " 'CASUSERHDFS(brrees)'\n",
              "\n",
              "[tableName]\n",
              "\n",
              " 'LINKSPROJ'\n",
              "\n",
              "[casTable]\n",
              "\n",
              " CASTable('LINKSPROJ', caslib='CASUSERHDFS(brrees)')\n",
              "\n",
              "+ Elapsed: 0.482s, user: 0.441s, sys: 0.948s, mem: 1.73e+03mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; caslib</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASUSERHDFS(brrees)</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; tableName</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>LINKSPROJ</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; casTable</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>CASTable('LINKSPROJ', caslib='CASUSERHDFS(brrees)')</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.482s</span> &#183; <span class=\"cas-user\">user 0.441s</span> &#183; <span class=\"cas-sys\">sys 0.948s</span> &#183; <span class=\"cas-memory\">mem 1.73e+03MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "0iPUs4rLOViO",
        "outputId": "edda4809-8a1e-400a-f615-6a12ccd2a6ed"
      },
      "source": [
        "#@title Q: What's the shortest path between any two known high-risk nodes?\n",
        "cluster.datastep.runCode(\n",
        "    code=\"\"\"\n",
        "      data linksProjUnwt;\n",
        "        set linksProj;\n",
        "        drop weight;\n",
        "      run;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "cluster.datastep.runCode(\n",
        "    code=\"\"\"\n",
        "      data nodesSubset1;\n",
        "        set nodesProj;\n",
        "        source = fraud_flag;\n",
        "        sink = fraud_flag;\n",
        "        keep node source sink;\n",
        "      run;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "cluster.network.shortestpath(\n",
        "    nodes='nodesProj',\n",
        "    links='linksProjUnwt',\n",
        "    nodesSubset='nodesSubset1',\n",
        "    linksVar={'vars':['link_type']},\n",
        "    outWeights={'name':'outSpWeights1', 'replace':True},\n",
        "    outPaths={'name':'outSpPaths1', 'replace':True}\n",
        ");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 15000.\n",
            "NOTE: The number of links in the input graph is 646703.\n",
            "NOTE: Processing shortest paths problem using 4304 threads across 100 machines.\n",
            "NOTE: Processing the shortest paths problem between 50 source nodes and 50 sink nodes.\n",
            "NOTE: Processing the shortest paths problem used 0.02 (cpu: 0.42) seconds.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "cellView": "form",
        "id": "_dy392-ORCMj",
        "outputId": "b811ed01-9e98-416d-cdec-d5ac20a07f35"
      },
      "source": [
        "#@title Q: What's the longest path between any two known high-risk nodes?\n",
        "\n",
        "longest = cluster.CASTable('outSpWeights1').nlargest(1,'path_weight')\n",
        "src = longest.loc[0, 'source']\n",
        "snk = longest.loc[0, 'sink']\n",
        "cluster.table.fetch(\n",
        "    table={'name':'outSpPaths1',\n",
        "           'where':f\"source EQ '{src}' AND sink EQ '{snk}'\"},\n",
        "    sortBy=['order'],\n",
        "    fetchVars=['order', 'from', 'to', 'link_type'],\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table OUTSPPATHS1\n",
              " \n",
              "           order                                               from  \\\n",
              " 0             0  customer_data~923CD977-C14B-4D0B-935D-EF1F5A85...   \n",
              " 1             1  customer_trans_nonmon~7A41D3B0-03EE-4FF7-B9D1-...   \n",
              " 2             2  customer_trans_nonmon~9B16E2FD-3FC0-4668-A9FA-...   \n",
              " 3             3  customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...   \n",
              " 4             4  customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...   \n",
              " \n",
              "                                                   to     link_type  \n",
              " 0  customer_trans_nonmon~7A41D3B0-03EE-4FF7-B9D1-...   SAME PERSON  \n",
              " 1  customer_trans_nonmon~9B16E2FD-3FC0-4668-A9FA-...  SAME LATLONG  \n",
              " 2  customer_trans~99AC5344-8C9B-480A-B290-73BD7CF...   SAME PERSON  \n",
              " 3  customer_trans~99AC5344-8C9B-480A-B290-73BD7CF...  SAME LATLONG  \n",
              " 4  customer_trans~E763AA6D-D8C5-4DA5-8CD4-456BAAB...   SAME DEVICE  \n",
              "\n",
              "+ Elapsed: 0.864s, user: 2.2s, sys: 5.61s, mem: 577mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table OUTSPPATHS1</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"order\">order</th>\n",
              "      <th title=\"from\">from</th>\n",
              "      <th title=\"to\">to</th>\n",
              "      <th title=\"link_type\">link_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>customer_data~923CD977-C14B-4D0B-935D-EF1F5A85...</td>\n",
              "      <td>customer_trans_nonmon~7A41D3B0-03EE-4FF7-B9D1-...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>customer_trans_nonmon~7A41D3B0-03EE-4FF7-B9D1-...</td>\n",
              "      <td>customer_trans_nonmon~9B16E2FD-3FC0-4668-A9FA-...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>customer_trans_nonmon~9B16E2FD-3FC0-4668-A9FA-...</td>\n",
              "      <td>customer_trans~99AC5344-8C9B-480A-B290-73BD7CF...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...</td>\n",
              "      <td>customer_trans~99AC5344-8C9B-480A-B290-73BD7CF...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>customer_trans~4AF6B079-258E-4A1B-BC61-623B95B...</td>\n",
              "      <td>customer_trans~E763AA6D-D8C5-4DA5-8CD4-456BAAB...</td>\n",
              "      <td>SAME DEVICE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.864s</span> &#183; <span class=\"cas-user\">user 2.2s</span> &#183; <span class=\"cas-sys\">sys 5.61s</span> &#183; <span class=\"cas-memory\">mem 577MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "RZT5h_6iUT-g",
        "outputId": "dcede1a5-5530-465d-b470-f28348e12866"
      },
      "source": [
        "#@title Q: What's the longest path between any one known high-risk node and any one unknown high-risk node?\n",
        "\n",
        "cluster.datastep.runCode(\n",
        "    code=\"\"\"\n",
        "      data nodesSubset2;\n",
        "        set nodesProj;\n",
        "        source = fraud_flag;\n",
        "        sink = 1;\n",
        "        keep node source sink;\n",
        "      run;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "cluster.network.shortestpath(\n",
        "    nodes='nodesProj',\n",
        "    links='linksProjUnwt',\n",
        "    nodesSubset='nodesSubset2',\n",
        "    linksVar={'vars':['link_type']},\n",
        "    outWeights={'name':'outSpWeights2', 'replace':True},\n",
        "    outPaths={'name':'outSpPaths2', 'replace':True}\n",
        ");\n",
        "\n",
        "longest = cluster.CASTable('outSpWeights2').nlargest(1,'path_weight')\n",
        "src = longest.loc[0, 'source']\n",
        "snk = longest.loc[0, 'sink']\n",
        "cluster.table.fetch(\n",
        "    table={'name':'outSpPaths2',\n",
        "           'where':f\"source EQ '{src}' AND sink EQ '{snk}'\"},\n",
        "    sortBy=['order'],\n",
        "    fetchVars=['order', 'from', 'to', 'link_type'],\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 15000.\n",
            "NOTE: The number of links in the input graph is 646703.\n",
            "NOTE: Processing shortest paths problem using 4304 threads across 100 machines.\n",
            "NOTE: Processing the shortest paths problem between 50 source nodes and 15000 sink nodes.\n",
            "NOTE: Processing the shortest paths problem used 0.01 (cpu: 2.24) seconds.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table OUTSPPATHS2\n",
              " \n",
              "            order                                               from  \\\n",
              " 0              0  customer_data~26F425B7-1A30-4510-B3EC-4327B79B...   \n",
              " 1              1  customer_data~26F425B7-1A30-4510-B3EC-4327B79B...   \n",
              " 2              2  customer_trans_nonmon~056B2C51-B1E4-4E3C-A07C-...   \n",
              " 3              3  customer_trans_nonmon~65BED266-7860-4929-9D11-...   \n",
              " 4              4  customer_trans~6CB3898D-F2DC-4C44-8261-BBAE36B...   \n",
              " 5              5  customer_data~F7047185-433B-4587-BD76-9BCE43EC...   \n",
              " 6              6  customer_data~8D75F2F5-6F7C-425C-A004-BD457613...   \n",
              " 7              7  customer_data~8D75F2F5-6F7C-425C-A004-BD457613...   \n",
              " 8              8  customer_trans_nonmon~AF4AFE89-0865-4BC2-8FE3-...   \n",
              " 9              9  customer_data~6870B172-BEC8-4D99-B620-2D56C09D...   \n",
              " 10            10  customer_data~6870B172-BEC8-4D99-B620-2D56C09D...   \n",
              " \n",
              "                                                    to     link_type  \n",
              " 0   customer_trans~327D921B-6C31-4E14-BDAD-2BFF585...  SAME ACCOUNT  \n",
              " 1   customer_trans_nonmon~056B2C51-B1E4-4E3C-A07C-...   SAME PERSON  \n",
              " 2   customer_trans_nonmon~65BED266-7860-4929-9D11-...   SAME PERSON  \n",
              " 3   customer_trans~8A5CBF3E-C548-487C-A5C7-E670703...   SAME PERSON  \n",
              " 4   customer_trans~8A5CBF3E-C548-487C-A5C7-E670703...  SAME LATLONG  \n",
              " 5   customer_trans~6CB3898D-F2DC-4C44-8261-BBAE36B...   SAME PERSON  \n",
              " 6   customer_data~F7047185-433B-4587-BD76-9BCE43EC...  SAME LATLONG  \n",
              " 7   customer_trans_nonmon~AF4AFE89-0865-4BC2-8FE3-...   SAME PERSON  \n",
              " 8   customer_trans_nonmon~DD679C8A-44DC-4446-92CD-...  SAME LATLONG  \n",
              " 9   customer_trans_nonmon~DD679C8A-44DC-4446-92CD-...   SAME PERSON  \n",
              " 10  customer_data~D8276D67-0CDD-4D9E-8356-A3F0C259...  SAME LATLONG  \n",
              "\n",
              "+ Elapsed: 0.596s, user: 2.33s, sys: 4.48s, mem: 744mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table OUTSPPATHS2</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"order\">order</th>\n",
              "      <th title=\"from\">from</th>\n",
              "      <th title=\"to\">to</th>\n",
              "      <th title=\"link_type\">link_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>customer_data~26F425B7-1A30-4510-B3EC-4327B79B...</td>\n",
              "      <td>customer_trans~327D921B-6C31-4E14-BDAD-2BFF585...</td>\n",
              "      <td>SAME ACCOUNT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>customer_data~26F425B7-1A30-4510-B3EC-4327B79B...</td>\n",
              "      <td>customer_trans_nonmon~056B2C51-B1E4-4E3C-A07C-...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>customer_trans_nonmon~056B2C51-B1E4-4E3C-A07C-...</td>\n",
              "      <td>customer_trans_nonmon~65BED266-7860-4929-9D11-...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>customer_trans_nonmon~65BED266-7860-4929-9D11-...</td>\n",
              "      <td>customer_trans~8A5CBF3E-C548-487C-A5C7-E670703...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>customer_trans~6CB3898D-F2DC-4C44-8261-BBAE36B...</td>\n",
              "      <td>customer_trans~8A5CBF3E-C548-487C-A5C7-E670703...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>customer_data~F7047185-433B-4587-BD76-9BCE43EC...</td>\n",
              "      <td>customer_trans~6CB3898D-F2DC-4C44-8261-BBAE36B...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>customer_data~8D75F2F5-6F7C-425C-A004-BD457613...</td>\n",
              "      <td>customer_data~F7047185-433B-4587-BD76-9BCE43EC...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>customer_data~8D75F2F5-6F7C-425C-A004-BD457613...</td>\n",
              "      <td>customer_trans_nonmon~AF4AFE89-0865-4BC2-8FE3-...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>customer_trans_nonmon~AF4AFE89-0865-4BC2-8FE3-...</td>\n",
              "      <td>customer_trans_nonmon~DD679C8A-44DC-4446-92CD-...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>customer_data~6870B172-BEC8-4D99-B620-2D56C09D...</td>\n",
              "      <td>customer_trans_nonmon~DD679C8A-44DC-4446-92CD-...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>customer_data~6870B172-BEC8-4D99-B620-2D56C09D...</td>\n",
              "      <td>customer_data~D8276D67-0CDD-4D9E-8356-A3F0C259...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.596s</span> &#183; <span class=\"cas-user\">user 2.33s</span> &#183; <span class=\"cas-sys\">sys 4.48s</span> &#183; <span class=\"cas-memory\">mem 744MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "cellView": "form",
        "id": "cZfhC0QuU2vp",
        "outputId": "76073658-0462-42e3-e711-14782d342d9a"
      },
      "source": [
        "#@title Q: Please identify the path between two nodes which contains the most number of edge types.\n",
        "cluster.network.shortestpath(\n",
        "    nodes='nodesProj',\n",
        "    links='linksProjUnwt',\n",
        "    linksVar={'vars':['link_type']},\n",
        "    outPaths={'name':'outSpPaths3', 'replace':True}\n",
        ");\n",
        "\n",
        "cluster.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "      create table distinctLinkTypes{options replace=true} as\n",
        "      select source, sink, count(distinct link_type) as \"count\"\n",
        "      from outSpPaths3\n",
        "      where source < sink\n",
        "      group by source, sink\n",
        "      ;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "most = cluster.CASTable('distinctLinkTypes').nlargest(1,'count')\n",
        "\n",
        "src = most.loc[0, 'source']\n",
        "snk = most.loc[0, 'sink']\n",
        "cnt = most.loc[0, 'count']\n",
        "\n",
        "tied = len(cluster.CASTable('distinctLinkTypes')[cluster.CASTable('distinctLinkTypes')['count']==cnt])\n",
        "\n",
        "print(f'Displaying one path with {cnt} link types.')\n",
        "print(f'There were {tied-1} other paths that also have {cnt} link types.')\n",
        "\n",
        "cluster.table.fetch(\n",
        "    table={'name':'outSpPaths3',\n",
        "           'where':f\"source EQ '{src}' AND sink EQ '{snk}'\"},\n",
        "    sortBy=['order'],\n",
        "    fetchVars=['order', 'from', 'to', 'link_type'],\n",
        "    format=True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 15000.\n",
            "NOTE: The number of links in the input graph is 646703.\n",
            "NOTE: Processing shortest paths problem using 4304 threads across 100 machines.\n",
            "NOTE: Processing the shortest paths problem between 15000 source nodes and 15000 sink nodes.\n",
            "NOTE: Processing the shortest paths problem used 2.32 (cpu: 1081.87) seconds.\n",
            "NOTE: Table DISTINCTLINKTYPES was created in caslib CASUSERHDFS(brrees) with 110506574 rows returned.\n",
            "Displaying one path with 7 link types.\n",
            "There were 319 other paths that also have 7 link types.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table OUTSPPATHS3\n",
              " \n",
              "           order                                               from  \\\n",
              " 0             0  customer_data~084BEAC3-435B-4BC7-AC38-46FC9AF8...   \n",
              " 1             1  customer_data~3D82321C-A334-4261-BF9F-AA53302D...   \n",
              " 2             2  customer_data~3D82321C-A334-4261-BF9F-AA53302D...   \n",
              " 3             3  customer_data~1DD61026-CF1A-4DB6-8549-2B8D8591...   \n",
              " 4             4  customer_data~1DD61026-CF1A-4DB6-8549-2B8D8591...   \n",
              " 5             5                          employee_data~723184885-0   \n",
              " 6             6  vendor_payments~08CB55A8-EBC0-4E5E-89AD-75E9D6...   \n",
              " \n",
              "                                                   to          link_type  \n",
              " 0  customer_trans~74C965CF-6FD2-4B50-9852-35F4A6F...       SAME LATLONG  \n",
              " 1  customer_trans~74C965CF-6FD2-4B50-9852-35F4A6F...        SAME PERSON  \n",
              " 2  customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...         SAME EMAIL  \n",
              " 3  customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...     SAME TELEPHONE  \n",
              " 4                          employee_data~723184885-0        SAME DEVICE  \n",
              " 5  vendor_payments~08CB55A8-EBC0-4E5E-89AD-75E9D6...       SAME ACCOUNT  \n",
              " 6  vendor_payments~A4E9A998-14D0-4D4D-8FE1-8686AC...  SAME ORGANISATION  \n",
              "\n",
              "+ Elapsed: 3.27s, user: 111s, sys: 32.3s, mem: 5.77e+04mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table OUTSPPATHS3</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"order\">order</th>\n",
              "      <th title=\"from\">from</th>\n",
              "      <th title=\"to\">to</th>\n",
              "      <th title=\"link_type\">link_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>customer_data~084BEAC3-435B-4BC7-AC38-46FC9AF8...</td>\n",
              "      <td>customer_trans~74C965CF-6FD2-4B50-9852-35F4A6F...</td>\n",
              "      <td>SAME LATLONG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>customer_data~3D82321C-A334-4261-BF9F-AA53302D...</td>\n",
              "      <td>customer_trans~74C965CF-6FD2-4B50-9852-35F4A6F...</td>\n",
              "      <td>SAME PERSON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>customer_data~3D82321C-A334-4261-BF9F-AA53302D...</td>\n",
              "      <td>customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...</td>\n",
              "      <td>SAME EMAIL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>customer_data~1DD61026-CF1A-4DB6-8549-2B8D8591...</td>\n",
              "      <td>customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...</td>\n",
              "      <td>SAME TELEPHONE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>customer_data~1DD61026-CF1A-4DB6-8549-2B8D8591...</td>\n",
              "      <td>employee_data~723184885-0</td>\n",
              "      <td>SAME DEVICE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>employee_data~723184885-0</td>\n",
              "      <td>vendor_payments~08CB55A8-EBC0-4E5E-89AD-75E9D6...</td>\n",
              "      <td>SAME ACCOUNT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>vendor_payments~08CB55A8-EBC0-4E5E-89AD-75E9D6...</td>\n",
              "      <td>vendor_payments~A4E9A998-14D0-4D4D-8FE1-8686AC...</td>\n",
              "      <td>SAME ORGANISATION</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 3.27s</span> &#183; <span class=\"cas-user\">user 111s</span> &#183; <span class=\"cas-sys\">sys 32.3s</span> &#183; <span class=\"cas-memory\">mem 5.77e+04MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GP3-VPHgX6b_"
      },
      "source": [
        "# Part 4: Risk Modeling Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ai_XAA0ZU1r"
      },
      "source": [
        "## Q: What's the approach to rank ordering the risk of the known high-risk nodes?\n",
        "\n",
        "A: The approach is to build a supervised risk classifier. Based on features created from the network (centrality, etc.). To validate the model, we create a stratefied split of the known labels: 50% for training and 50% for validation. For the classifier, we train a forest model. After achieving good performance on the prediction of validation labels, we re-score the entire set of nodes. Now, the known high-risk nodes can be ranked by the output of this risk model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "v_rbcQNbTRvi"
      },
      "source": [
        "#@title Set paramters\n",
        "seed=1000\n",
        "train_pct=50\n",
        "n_trees=300\n",
        "max_level=6\n",
        "leaf_size=6\n",
        "n_bins=60\n",
        "bootstrap=0.9\n",
        "feature_vars = [\n",
        "                'centr_degree_out',\n",
        "                'centr_degree_out_wt',\n",
        "                'centr_between_wt',\n",
        "                'centr_close_wt',\n",
        "                'centr_pagerank_wt',\n",
        "                'centr_cluster'\n",
        "]\n",
        "target = 'fraud_flag'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "W1n2DFDwXvCQ",
        "outputId": "91c71a78-a13a-49cb-d297-b75769bae030"
      },
      "source": [
        "#@title Test validation split\n",
        "s.sampling.stratified(\n",
        "    seed=seed,\n",
        "    samppct=train_pct,\n",
        "    partind=True,\n",
        "    table={'name':'nodesCentr', 'groupBy':'fraud_flag'},\n",
        "    output={\n",
        "        'casout':{'name':'nodesPart', 'replace':True},\n",
        "        'copyVars':'all',\n",
        "        'partIndName':'train'\n",
        "    }\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Stratified sampling is in effect.\n",
            "NOTE: Using SEED=1000 for sampling.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[STRAFreq]\n",
              "\n",
              " Frequencies\n",
              " \n",
              "    ByGrpID    fraud_flag  NObs  NSamp\n",
              " 0        0             0  6950   3475\n",
              " 1        1             1    50     25\n",
              "\n",
              "[OutputCasTables]\n",
              "\n",
              "             casLib       Name Label  Rows  Columns  \\\n",
              " 0  CASUSER(brrees)  nodesPart        7000       10   \n",
              " \n",
              "                                           casTable  \n",
              " 0  CASTable('nodesPart', caslib='CASUSER(brrees)')  \n",
              "\n",
              "+ Elapsed: 0.0213s, user: 0.015s, sys: 0.007s, mem: 41.8mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; STRAFreq</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Frequencies</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Index\">ByGrpID</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "      <th title=\"Number of Obs\">NObs</th>\n",
              "      <th title=\"Number of Samples\">NSamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6950</td>\n",
              "      <td>3475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"CAS Library\">casLib</th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Label\">Label</th>\n",
              "      <th title=\"Number of Rows\">Rows</th>\n",
              "      <th title=\"Number of Columns\">Columns</th>\n",
              "      <th title=\"Table\">casTable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CASUSER(brrees)</td>\n",
              "      <td>nodesPart</td>\n",
              "      <td></td>\n",
              "      <td>7000</td>\n",
              "      <td>10</td>\n",
              "      <td>CASTable('nodesPart', caslib='CASUSER(brrees)')</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0213s</span> &#183; <span class=\"cas-user\">user 0.015s</span> &#183; <span class=\"cas-sys\">sys 0.007s</span> &#183; <span class=\"cas-memory\">mem 41.8MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "JLAEJ8LLb9Bc",
        "outputId": "01e0329f-2be6-4912-d51c-4c6c1bdf8a99"
      },
      "source": [
        "#@title Train the model\n",
        "r = s.decisionTree.forestTrain(\n",
        "    table={'name':'nodesPart', 'where':'train EQ 1'},\n",
        "    inputs=feature_vars,\n",
        "    target=target,\n",
        "    seed=seed,\n",
        "    nTree=n_trees,\n",
        "    quantileBin=True,\n",
        "    varImp=True,\n",
        "    maxLevel=max_level,\n",
        "    leafSize=leaf_size,\n",
        "    nBins=n_bins,\n",
        "    bootstrap=bootstrap,\n",
        "    casout={'name':'forestModel', 'replace':True}\n",
        ")\n",
        "r.DTreeVarImpInfo"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Forest for NODESPART\n",
              "\n",
              "              Variable  Importance       Std\n",
              "0     centr_between_wt    2.812684  1.506421\n",
              "1        centr_cluster    2.451108  1.709825\n",
              "2  centr_degree_out_wt    2.298022  1.557358\n",
              "3       centr_close_wt    1.722334  0.928229\n",
              "4    centr_pagerank_wt    1.328639  0.941016\n",
              "5     centr_degree_out    0.960482  1.034041"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Forest for NODESPART</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"Analysis Variable\">Variable</th>\n",
              "      <th title=\"Importance\">Importance</th>\n",
              "      <th title=\"Std\">Std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>centr_between_wt</td>\n",
              "      <td>2.812684</td>\n",
              "      <td>1.506421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>centr_cluster</td>\n",
              "      <td>2.451108</td>\n",
              "      <td>1.709825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>centr_degree_out_wt</td>\n",
              "      <td>2.298022</td>\n",
              "      <td>1.557358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>centr_close_wt</td>\n",
              "      <td>1.722334</td>\n",
              "      <td>0.928229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>centr_pagerank_wt</td>\n",
              "      <td>1.328639</td>\n",
              "      <td>0.941016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>centr_degree_out</td>\n",
              "      <td>0.960482</td>\n",
              "      <td>1.034041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "mXqx1GKWdqSo",
        "outputId": "f0016c26-c915-46a4-b1e7-484aa80ff302"
      },
      "source": [
        "#@title Score using the validation set\n",
        "s.decisiontree.forestScore(\n",
        "    modelTable='forestModel',\n",
        "    table={'name':'nodesPart', 'where':'train NE 1'},\n",
        "    target=target,\n",
        "    casout={'name':'forestVldScored', 'replace':True},\n",
        "    copyVars=['node']+[target]+feature_vars+['train']\n",
        ")\n",
        "\n",
        "s.fedsql.execdirect(\n",
        "    query=f\"\"\"\n",
        "      create table forestVldEval{{options replace=true}} as\n",
        "      select node, {target}, _RF_PredMean_ as \"risk_score\"\n",
        "      from forestVldScored\n",
        "      ;\n",
        "    \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Table FORESTVLDEVAL was created in caslib CASUSER(brrees) with 3500 rows returned.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "+ Elapsed: 0.019s, user: 0.019s, sys: 0.033s, mem: 25.6mb"
            ],
            "text/html": [
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.019s</span> &#183; <span class=\"cas-user\">user 0.019s</span> &#183; <span class=\"cas-sys\">sys 0.033s</span> &#183; <span class=\"cas-memory\">mem 25.6MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "p4_VnvsZfGEA",
        "outputId": "a446f818-b9c4-43f6-9dd7-779888ccc2be"
      },
      "source": [
        "#@title Evaluate the model\n",
        "y_true = s.CASTable('forestVldEval').to_frame()[target]\n",
        "y_pred = s.CASTable('forestVldEval').to_frame()['risk_score']\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.plot(fpr, tpr, 'y', label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],'r--')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()\n",
        "\n",
        "precision, recall, threshold = precision_recall_curve(y_true, y_pred)\n",
        "average_precision = average_precision_score(y_true, y_pred)\n",
        "step_kwargs = ({'step': 'post'} if 'step' in signature(plt.fill_between).parameters else {})\n",
        "plt.step(recall, precision, color='r', alpha=0.2, where='post')\n",
        "plt.fill_between(recall, precision, alpha=0.2, color='r', **step_kwargs)\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "6J_QXCIXkbKQ",
        "outputId": "d3ffeafd-cfd8-4260-a727-804523cb7554"
      },
      "source": [
        "#@title Score all nodes\n",
        "s.decisiontree.forestScore(\n",
        "    modelTable='forestModel',\n",
        "    table={'name':'nodesPart'},\n",
        "    target=target,\n",
        "    casout={'name':'forestScored', 'replace':True},\n",
        "    copyVars=['node']+[target]+feature_vars+['train']\n",
        ")\n",
        "\n",
        "s.datastep.runCode(\n",
        "    code=f\"\"\"\n",
        "      data forestScored;\n",
        "        set forestScored;\n",
        "        rename _RF_PredMean_=risk_score;\n",
        "      ;\n",
        "    \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[InputCasTables]\n",
              "\n",
              "             casLib          Name  Rows  Columns  \\\n",
              " 0  CASUSER(brrees)  forestScored  7000       14   \n",
              " \n",
              "                                             casTable  \n",
              " 0  CASTable('forestScored', caslib='CASUSER(brree...  \n",
              "\n",
              "[OutputCasTables]\n",
              "\n",
              "             casLib          Name  Rows  Columns  Append Promoted  \\\n",
              " 0  CASUSER(brrees)  forestScored  7000       14     NaN        N   \n",
              " \n",
              "                                             casTable  \n",
              " 0  CASTable('forestScored', caslib='CASUSER(brree...  \n",
              "\n",
              "+ Elapsed: 0.0152s, user: 0.017s, sys: 0.022s, mem: 10.8mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; InputCasTables</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"CAS Library\">casLib</th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Number of Rows\">Rows</th>\n",
              "      <th title=\"Number of Columns\">Columns</th>\n",
              "      <th title=\"Table\">casTable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CASUSER(brrees)</td>\n",
              "      <td>forestScored</td>\n",
              "      <td>7000</td>\n",
              "      <td>14</td>\n",
              "      <td>CASTable('forestScored', caslib='CASUSER(brree...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-results-key\"><hr/><b>&#167; OutputCasTables</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"CAS Library\">casLib</th>\n",
              "      <th title=\"Name\">Name</th>\n",
              "      <th title=\"Number of Rows\">Rows</th>\n",
              "      <th title=\"Number of Columns\">Columns</th>\n",
              "      <th title=\"Number of Rows Appended\">Append</th>\n",
              "      <th title=\"Promoted\">Promoted</th>\n",
              "      <th title=\"Table\">casTable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CASUSER(brrees)</td>\n",
              "      <td>forestScored</td>\n",
              "      <td>7000</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>N</td>\n",
              "      <td>CASTable('forestScored', caslib='CASUSER(brree...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0152s</span> &#183; <span class=\"cas-user\">user 0.017s</span> &#183; <span class=\"cas-sys\">sys 0.022s</span> &#183; <span class=\"cas-memory\">mem 10.8MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "psYIXqHJgQ50",
        "outputId": "7b2cdda1-c845-4d50-aaf3-84516afef834"
      },
      "source": [
        "#@title Show 10 nodes with highest risk\n",
        "s.table.fetch(\n",
        "    table='forestScored',\n",
        "    fetchVars=['node', 'risk_score', 'fraud_flag'],\n",
        "    sortby=[{'name':'risk_score', 'order':'DESCENDING'}],\n",
        "    to=10,\n",
        "    format=True\n",
        ") "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table FORESTSCORED\n",
              " \n",
              "                                                 node    risk_score  \\\n",
              " 0  customer_data~5915A6E6-D2ED-4AD6-825F-AB0FC7F0...   0.720254093   \n",
              " 1  customer_data~6121DEC5-C0FB-4EC2-9D12-CDE78D19...   0.637425059   \n",
              " 2  customer_data~1CD4C120-E572-4222-8CD1-C83A0E26...  0.6355966559   \n",
              " 3  customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...  0.5983528561   \n",
              " 4  customer_data~F971053F-012F-4DC9-BA10-1ED3BBD0...  0.5983528561   \n",
              " 5  customer_data~F7B6EB9F-AF36-426E-8610-51B5BE84...  0.5983528561   \n",
              " 6  customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...  0.5983528561   \n",
              " 7  customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...  0.5983528561   \n",
              " 8  customer_data~F4B4D286-A355-4ABA-AB3E-3380AE44...  0.5930935534   \n",
              " 9  customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...  0.5879303456   \n",
              " \n",
              "      fraud_flag  \n",
              " 0             1  \n",
              " 1             1  \n",
              " 2             1  \n",
              " 3             1  \n",
              " 4             1  \n",
              " 5             1  \n",
              " 6             0  \n",
              " 7             0  \n",
              " 8             1  \n",
              " 9             0  \n",
              "\n",
              "+ Elapsed: 0.0038s, user: 0.003s, mem: 3.72mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table FORESTSCORED</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"node\">node</th>\n",
              "      <th title=\"risk_score\">risk_score</th>\n",
              "      <th title=\"fraud_flag\">fraud_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>customer_data~5915A6E6-D2ED-4AD6-825F-AB0FC7F0...</td>\n",
              "      <td>0.720254093</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>customer_data~6121DEC5-C0FB-4EC2-9D12-CDE78D19...</td>\n",
              "      <td>0.637425059</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>customer_data~1CD4C120-E572-4222-8CD1-C83A0E26...</td>\n",
              "      <td>0.6355966559</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>customer_data~0B735E36-91B8-4B0E-99A3-53352BDC...</td>\n",
              "      <td>0.5983528561</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>customer_data~F971053F-012F-4DC9-BA10-1ED3BBD0...</td>\n",
              "      <td>0.5983528561</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>customer_data~F7B6EB9F-AF36-426E-8610-51B5BE84...</td>\n",
              "      <td>0.5983528561</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>customer_data~C6AEF96F-9247-4D25-BEA1-2A576FEC...</td>\n",
              "      <td>0.5983528561</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>customer_data~C12C8C63-38CC-45E8-AE5B-03B5CC07...</td>\n",
              "      <td>0.5983528561</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>customer_data~F4B4D286-A355-4ABA-AB3E-3380AE44...</td>\n",
              "      <td>0.5930935534</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>customer_trans~E557B346-27CD-45A5-A6CC-EA9EF72...</td>\n",
              "      <td>0.5879303456</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.0038s</span> &#183; <span class=\"cas-user\">user 0.003s</span> &#183; <span class=\"cas-memory\">mem 3.72MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjvqsqLOnLUN"
      },
      "source": [
        "## Q: How would you leverage the graph to quantify risk by association given the known high-risk nodes provided? Please provide both a qualitative and quantitative approach.\n",
        "\n",
        "A: Qualitatively, Visual Investigator's Visual Graph Query Builder tool may be used to view the neighborhood surrounding a high risk node, indentify a pattern in topology and/or attributes, and search for other instances of that pattern in all of the graph.\n",
        "\n",
        "Quantitatively, weighted shortestPath may be used to calculate distances from each high risk node to every other node in the graph. By aggregating the results, we can get the distance to nearest known high risk for each node in the graph. Because the original weights are proportional to strength of association, we invert the weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "0mZbHX8VoUXQ",
        "outputId": "b4d1195a-3769-4736-ae31-d814b9859b38"
      },
      "source": [
        "#@title Compute proximity to high risk nodes\n",
        "s.datastep.runCode(\n",
        "    code=\"\"\"\n",
        "      data nodesSubset;\n",
        "        set nodesProj;\n",
        "        source = fraud_flag;\n",
        "        sink = 1;\n",
        "        keep node source sink;\n",
        "      run;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "s.network.shortestpath(\n",
        "    nodes='nodesProj',\n",
        "    links={\n",
        "        'name':'linksProj',\n",
        "        'computedVars':['inverseWeight'],\n",
        "        'computedVarsProgram':'inverseWeight=1.0/weight'\n",
        "    },\n",
        "    linksVar={\n",
        "        'weight':'inverseWeight'\n",
        "    },\n",
        "    nodesSubset='nodesSubset',\n",
        "    outWeights={'name':'outSpWeights', 'replace':True}\n",
        ");\n",
        "\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "      create table proximityToKnownHighRisk{options replace=true} as\n",
        "      select sink as \"node\",\n",
        "              min(path_weight) as \"min_proximity\",\n",
        "              min(b.fraud_flag) as fraud_flag,\n",
        "              min(b.risk_score) as risk_score\n",
        "      from outSpWeights as a\n",
        "      join forestScored as b\n",
        "      on a.sink = b.node\n",
        "      group by sink\n",
        "      ;\n",
        "    \"\"\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-7b008ec8d9b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#@title Compute proximity to high risk nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m s.datastep.runCode(\n\u001b[0m\u001b[1;32m      3\u001b[0m     code=\"\"\"\n\u001b[1;32m      4\u001b[0m       \u001b[0mdata\u001b[0m \u001b[0mnodesSubset\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mset\u001b[0m \u001b[0mnodesProj\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 's' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLf182j-qyay"
      },
      "source": [
        "## Q: How many additional nodes can be identified based on linkage with known high-risk customer and transactions?\n",
        "\n",
        "A: We will examine the distribution of proximity to known high risk and determine an appropriate theshold."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "MuHSToHDqru9",
        "outputId": "b2cb04db-39b1-402c-9288-9ca3871428e2"
      },
      "source": [
        "#@title View histogram of the proximity score\n",
        "plt.hist(\n",
        "    s.CASTable('proximityToKnownHighRisk').to_frame()['min_proximity'],\n",
        "    bins=25,\n",
        "    \n",
        ");\n",
        "\n",
        "proximity_threshold = np.percentile(s.CASTable('proximityToKnownHighRisk').to_frame()['min_proximity'], 10.0)\n",
        "\n",
        "print(f'The selected threshold, based on the 10th percentile is {proximity_threshold}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The selected threshold, based on the 10th percentile is 1.0.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "viCa1rQZsuoT",
        "outputId": "1df9d531-c442-4146-bf14-bc69869f5b98"
      },
      "source": [
        "#@title Count additional nodes flagged by proximity\n",
        "r=s.fedsql.execdirect(\n",
        "    query=f\"\"\"\n",
        "      select count(*)\n",
        "      from proximityToKnownHighRisk\n",
        "      where min_proximity < {proximity_threshold} AND fraud_flag = 0\n",
        "      ;\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "additional_nodes = r['Result Set'].loc[0, 'COUNT']\n",
        "\n",
        "print(f'{additional_nodes} additional nodes were flagged because of proximity to known high risk.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 additional nodes were flagged because of proximity to known high risk.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGCXxCb3t0Mk"
      },
      "source": [
        "## Q: How many suspicious clusters can be identified in the established graph network? What's the approach and outputs of the clustering process?\n",
        "\n",
        "A: To perform clustering, we use the Louvain algorithm for community detection. Once the network in broken down into communities, we can aggregate both the mean and sum of risk scores among each community."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqnb5yHUucYR",
        "outputId": "33872fea-470f-4bb4-ca15-1d48a3f87ead"
      },
      "source": [
        "#@title Run community detection\n",
        "s.network.community(\n",
        "    nodes='forestScored',\n",
        "    links='linksProj2',\n",
        "    outNodes={'name':'nodesComm', 'replace':True},\n",
        "    nodesVar={'vars':['fraud_flag', 'risk_score']} \n",
        ");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: The number of nodes in the input graph is 7000.\n",
            "NOTE: The number of links in the input graph is 7350.\n",
            "NOTE: Processing community detection using 1 threads across 1 machines.\n",
            "NOTE: At resolution=1, the community algorithm found 4357 communities with modularity=0.946948.\n",
            "NOTE: Processing community detection used 0.01 (cpu: 0.01) seconds.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZbPAvvMvcZC",
        "outputId": "1b025436-59c8-464c-e471-f6eca377410f"
      },
      "source": [
        "#@title Aggregate risk by community\n",
        "s.fedsql.execdirect(\n",
        "    query=\"\"\"\n",
        "      create table communityRisk{options replace=true} as\n",
        "      select community_0,\n",
        "              count(*) as \"nodes\",\n",
        "              mean(risk_score) as \"mean_risk\",\n",
        "              sum(risk_score) as \"sum_risk\" \n",
        "      from nodesComm\n",
        "      group by community_0\n",
        "    \"\"\"\n",
        ")\n",
        "\n",
        "mean_threshold = np.percentile(s.CASTable('communityRisk').to_frame()['mean_risk'], 99.7)\n",
        "sum_threshold = np.percentile(s.CASTable('communityRisk').to_frame()['sum_risk'], 99.7)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NOTE: Table COMMUNITYRISK was created in caslib CASUSER(brrees) with 4357 rows returned.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "YrosIOt3v77w",
        "outputId": "3bd50eae-25b6-4bca-a6a9-9494e2cf43a5"
      },
      "source": [
        "#@title \n",
        "r = s.table.fetch(\n",
        "    table={'name':'communityRisk',\n",
        "           'where':f'mean_risk GT {mean_threshold} AND sum_risk GT {sum_threshold}'\n",
        "           },\n",
        "    format=True,\n",
        "    sortBy=[{'name':'sum_risk', 'order':'DESCENDING'}]\n",
        ")\n",
        "\n",
        "num_risky = len(r.Fetch)\n",
        "\n",
        "print(f'There are {num_risky} risky communities based on both mean and sum risk in the 99.7th percentile')\n",
        "r"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 11 risky communities based on both mean and sum risk in the 99.7th percentile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Fetch]\n",
              "\n",
              " Selected Rows from Table COMMUNITYRISK\n",
              " \n",
              "      community_0         nodes     mean_risk      sum_risk\n",
              " 0             18            98  0.0873351909  8.5588487057\n",
              " 1             58            63  0.0920042831  5.7962698344\n",
              " 2             13            76  0.0647630527  4.9219920074\n",
              " 3            460            52  0.0880164765  4.5768567761\n",
              " 4            145            76  0.0489254522  3.7183343684\n",
              " 5            255            48   0.056890262  2.7307325752\n",
              " 6            705            53  0.0361751961  1.9172853938\n",
              " 7           2509             5  0.2377906879  1.1889534394\n",
              " 8           4125             5  0.2377906879  1.1889534394\n",
              " 9           2964             5  0.2377906879  1.1889534394\n",
              " 10           173            31   0.032635355  1.0116960053\n",
              "\n",
              "+ Elapsed: 0.00162s, user: 0.002s, mem: 3.81mb"
            ],
            "text/html": [
              "<div class=\"cas-results-key\"><b>&#167; Fetch</b></div>\n",
              "<div class=\"cas-results-body\">\n",
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\"><caption>Selected Rows from Table COMMUNITYRISK</caption>\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th title=\"\"></th>\n",
              "      <th title=\"community_0\">community_0</th>\n",
              "      <th title=\"nodes\">nodes</th>\n",
              "      <th title=\"mean_risk\">mean_risk</th>\n",
              "      <th title=\"sum_risk\">sum_risk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18</td>\n",
              "      <td>98</td>\n",
              "      <td>0.0873351909</td>\n",
              "      <td>8.5588487057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>58</td>\n",
              "      <td>63</td>\n",
              "      <td>0.0920042831</td>\n",
              "      <td>5.7962698344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>76</td>\n",
              "      <td>0.0647630527</td>\n",
              "      <td>4.9219920074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>460</td>\n",
              "      <td>52</td>\n",
              "      <td>0.0880164765</td>\n",
              "      <td>4.5768567761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>145</td>\n",
              "      <td>76</td>\n",
              "      <td>0.0489254522</td>\n",
              "      <td>3.7183343684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>255</td>\n",
              "      <td>48</td>\n",
              "      <td>0.056890262</td>\n",
              "      <td>2.7307325752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>705</td>\n",
              "      <td>53</td>\n",
              "      <td>0.0361751961</td>\n",
              "      <td>1.9172853938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2509</td>\n",
              "      <td>5</td>\n",
              "      <td>0.2377906879</td>\n",
              "      <td>1.1889534394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4125</td>\n",
              "      <td>5</td>\n",
              "      <td>0.2377906879</td>\n",
              "      <td>1.1889534394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2964</td>\n",
              "      <td>5</td>\n",
              "      <td>0.2377906879</td>\n",
              "      <td>1.1889534394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>173</td>\n",
              "      <td>31</td>\n",
              "      <td>0.032635355</td>\n",
              "      <td>1.0116960053</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "<div class=\"cas-output-area\"></div>\n",
              "<p class=\"cas-results-performance\"><small><span class=\"cas-elapsed\">elapsed 0.00162s</span> &#183; <span class=\"cas-user\">user 0.002s</span> &#183; <span class=\"cas-memory\">mem 3.81MB</span></small></p>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    }
  ]
}