tutorials/Ensmallen_Automatic_Graph_Retieval_KGOBO.ipynb

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    "<a href=\"https://colab.research.google.com/github/AnacletoLAB/grape/blob/main/tutorials/Ensmallen_Automatic_Graph_Retieval_KGOBO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vxyX_zCoob11"
   },
   "source": [
    "# Ensmallen Automatic Graph Retrieval - KGOBO\n",
    "In this tutorial we will explore how to use the graphs that can be automatically retrieved by [Ensmallen](https://github.com/AnacletoLAB/ensmallen) from [OboFoundry](http://www.obofoundry.org/) through [Justin Reese](https://github.com/justaddcoffee) and [Harry Caufield](https://github.com/caufieldjh)'s [KGX](https://github.com/biolink/kgx)-based pipeline, [KG-OBO](https://github.com/Knowledge-Graph-Hub/kg-obo).\n",
    "\n",
    "The graphs available are those that can be currently converted from the OBO format to KGX format, and will be downloaded from the [kg-obo website](https://kg-hub.berkeleybop.io/kg-obo/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q5jLnV8fqHmQ"
   },
   "source": [
    "## Installing GRAPE\n",
    "To install the [GRAPE library](https://github.com/AnacletoLAB/grape), as usual simply run:\n",
    "\n",
    "```bash\n",
    "pip install grape\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "bpLUocZuoQPd"
   },
   "outputs": [],
   "source": [
    "!pip install -qU grape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UtJoXtrBqVHY"
   },
   "source": [
    "## Which OBO graphs are available?\n",
    "To get a list of all the OBO graphs currently available from Ensmallen, we can quickly get the dataframe with all of the graphs data and query it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "bOIROSwrqR_i"
   },
   "outputs": [],
   "source": [
    "from grape.datasets import get_available_graphs_from_repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "9ZZgcgE-ql5a",
    "outputId": "feed4ec8-8092-40a7-bcce-f586c364cee9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['MOD', 'FBBT', 'BTO', 'CHMO', 'OBA', 'PSO', 'OGSF', 'MCO', 'OPMI', 'FBDV', 'CEPH', 'MPATH', 'SPD', 'OMIT', 'VT', 'EHDAA2', 'FLOPO', 'WBLS', 'RXNO', 'OMP', 'ERO', 'GNO', 'XCO', 'AMPHX', 'EPIO', 'CLYH', 'OOSTT', 'FYPO', 'NCRO', 'IAO', 'GEO', 'EXO', 'SWO', 'OBCS', 'ENVO', 'SYMP', 'TAXRANK', 'APO', 'CLO', 'CMO', 'OHMI', 'HSO', 'FBBI', 'OBI', 'CDAO', 'MFMO', 'CRO', 'CHEMINF', 'MP', 'DUO', 'LABO', 'OLATDV', 'MPIO', 'CHEBI', 'AEO', 'ADO', 'FOBI', 'GENO', 'SBO', 'TO', 'UO', 'MOP', 'CHIRO', 'OGMS', 'NCBITAXON', 'FOODON', 'PW', 'FOVT', 'XPO', 'ZFS', 'RS', 'CTO', 'OMO', 'FIX', 'MAMO', 'VTO', 'UBERON', 'MFOMD', 'BFO', 'HTN', 'PORO', 'PROCO', 'AISM', 'WBBT', 'HAO', 'SO', 'RO', 'MONDO', 'DDPHENO', 'IDOMAL', 'MAXO', 'FBCV', 'TRANS', 'PSDO', 'SCDO', 'LEPAO', 'ONTONEO', 'DRON', 'RBO', 'NCIT', 'FMA', 'REX', 'COB', 'SIBO', 'PDRO', 'OGG', 'XLMOD', 'HANCESTRO', 'GO', 'MF', 'GSSO', 'UPHENO', 'PLANA', 'OAE', 'MMUSDV', 'MS', 'APOLLO_SV', 'HSAPDV', 'VO', 'MIRO', 'EMAPA', 'GECKO', 'CARO', 'GENEPIO', 'TADS', 'FAO', 'CVDO', 'ECAO', 'OHPI', 'OPL', 'TGMA', 'BCO', 'ICO', 'ZECO', 'PHIPO', 'PDUMDV', 'ARO', 'OARCS', 'PCL', 'CTENO', 'PLANP', 'DOID', 'OMRSE', 'PPO', 'OVAE', 'ZP', 'STATO', 'ONE', 'ECTO', 'XAO', 'MIAPA', 'MI', 'ECOCORE', 'MMO', 'EUPATH', 'OBIB', 'IDO', 'SEPIO', 'TTO', 'PR', 'NBO', 'WBPHENOTYPE', 'PECO', 'GAZ', 'CIO', 'INO', 'CLAO', 'UPA', 'NOMEN', 'ZFA', 'DISDRIV', 'CIDO', 'COLAO', 'KISAO', 'MA', 'PO', 'CDNO', 'ONS', 'OHD', 'VARIO', 'AGRO', 'DIDEO', 'TXPO', 'PATO', 'HOM', 'ECO', 'ICEO', 'DDANAT', 'BSPO', 'MRO', 'PCO', 'EPSO', 'ORNASEQ', 'HP', 'DPO', 'CL', 'MFOEM'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_available_graphs_from_repository(\"kgobo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nNAFUR39quxS"
   },
   "source": [
    "## How to load up a graph\n",
    "To retrieve one of the available OBO graphs, as per any other repository, you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "3Zx_iKFfqrqf"
   },
   "outputs": [],
   "source": [
    "from grape.datasets.kgobo import ZFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "23026049c8034e6388a9de91982bae78",
      "4410c716fcc0455187696dfb3f2e957c",
      "aedf58cf5fd34871b9e60c317456a8ae",
      "ac63feeac3e341ffa779b0c051ad0f11",
      "a67b84fc804a4753b6ccdff038c7e401",
      "11dead33757048139b3d8830e24f4165",
      "d6af361081f84dcfb6ea1457f54f3f2b",
      "e493cc2916aa4672929d8d30068fce2e",
      "efc2ae886fc4415296bf3e9666942c20",
      "afa115b59a4e47cc956f5191b4e7b6d9",
      "acb43b3d321c43d3840cefd7c223e02e"
     ]
    },
    "id": "rlDrI-RirCkX",
    "outputId": "0ccb3421-c29a-45a6-e4cf-7559e60c3f0b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      },
      "text/plain": [
       "Downloading to /bfd/graphs/kgob...fs_kgx_tsv.tar.gz:   0%|                                                    …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = ZFS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1vLCKTAJzKVC"
   },
   "source": [
    "Let's get the graph report:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "id": "VWCiwH3prEde",
    "outputId": "184e0caa-a677-4b52-ab4a-bb4b3837810e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"graph-report\"><style>.graph-report li {margin: 0.5em 0 0.5em 0;}.graph-report .paragraph {text-align: justify;word-break: break-all;}.graph-report .small-columns {column-count: 4;column-gap: 2em;}.graph-report .medium-columns {column-count: 3;column-gap: 2em;}.graph-report .large-columns {column-count: 2;column-gap: 2em;}.graph-report .single-column {}@media only screen and (max-width: 600px) {.graph-report .small-columns {column-count: 1;}.graph-report .medium-columns {column-count: 1;}.graph-report .large-columns {column-count: 1;}}@media only screen and (min-width: 600px) and (max-width: 800px) {.graph-report .small-columns {column-count: 2;}.graph-report .medium-columns {column-count: 1;}.graph-report .large-columns {column-count: 1;}}@media only screen and (min-width: 800px) and (max-width: 1024px) {.graph-report .small-columns {column-count: 3;}.graph-report .medium-columns {column-count: 2;}.graph-report .large-columns {column-count: 1;}}</style><h2>ZFS</h2><p class=\"paragraph\">The undirected graph ZFS has 77 homogeneous nodes and 149 heterogeneous edges. The graph contains 20 connected components (of which 15 are disconnected nodes), with the largest one containing 54 nodes and the smallest one containing a single node. The RAM requirements for the nodes and edges data structures are 9.05KB and 536B respectively.</p><h3 style=\"margin: 1em 0 0 0;\">Degree centrality</h3><p class=\"paragraph\">The minimum node degree is 0, the maximum node degree is 53, the mode degree is 4, the mean degree is 3.87 and the node degree median is 4.</p><p class=\"paragraph\">The nodes with the highest degree centrality are <a href='http://purl.obolibrary.org/obo/ZFS_0100000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0100000'>OBO:ZFS_0100000</a> (degree 53 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000045' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000045'>OBO:ZFS_0000045</a> (degree 12 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000048' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000048'>OBO:ZFS_0000048</a> (degree 10 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000049' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000049'>OBO:ZFS_0000049</a> (degree 9 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/ZFS_0000047' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000047'>OBO:ZFS_0000047</a> (degree 9 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p><h3 style=\"margin: 1em 0 0 0;\">Node types</h3><p class=\"paragraph\">The graph has a single node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>. The RAM requirement for the node types data structure is 4.31KB.</p><h4 style=\"margin: 1em 0 0 0;\">Homogeneous node types</h4><p class=\"paragraph\">Homogeneous node types are node types that are assigned to all the nodes in the graph, making the node type relatively meaningless, as it adds no more information than the fact that the node is in the graph. The graph contains a homogeneous node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>.</p><h3 style=\"margin: 1em 0 0 0;\">Edge types</h3><p class=\"paragraph\">The graph has 4 edge types, which are <a href='https://biolink.github.io/biolink-model/docs/subclass_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:subclass_of'>biolink:subclass_of</a> (112 edges, 37.58%), <a href='https://biolink.github.io/biolink-model/docs/preceded_by.html' target='_blank' title='Go to BioLink to get more informations about biolink:preceded_by'>biolink:preceded_by</a> (100 edges, 33.56%), <a href='https://biolink.github.io/biolink-model/docs/part_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:part_of'>biolink:part_of</a> (84 edges, 28.19%) and owl:versionIRI (2 edges, 0.67%). The RAM requirement for the edge types data structure is 1.85KB.</p><h3 style=\"margin: 1em 0 0 0;\">Topological Oddities</h3><p class=\"paragraph\">A topological oddity is a set of nodes in the graph that <i>may be derived</i> by an error during the generation of the edge list of the graph and, depending on the task, could bias the results of topology-based models. In the following paragraph, we will describe the detected topological oddities.</p><h4 style=\"margin: 1em 0 0 0;\">Singleton nodes</h4><p class=\"paragraph\">A singleton node is a node disconnected from all other nodes. We have detected 15 singleton nodes in the graph, involving a total of 15 nodes (19.48%). The detected singleton nodes are:</p><ul class=\"small-columns\"><li><p class=\"paragraph\"><a href='http://purl.obolibrary.org/obo/BFO_0000050' target='_blank' title='Go to Basic Formal Ontology to get more informations about BFO:0000050'>BFO:0000050</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasOBOFormatVersion' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasOBOFormatVersion'>OIO:hasOBOFormatVersion</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#creation_date' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:creation_date'>OIO:creation_date</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#shorthand' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:shorthand'>OIO:shorthand</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://purl.obolibrary.org/obo/IAO_0000115' target='_blank' title='Go to Information Artifact Ontology to get more informations about IAO:0000115'>IAO:0000115</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#saved-by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:saved-by'>OIO:saved-by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#id' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:id'>OIO:id</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.w3.org/2000/01/rdf-schema#comment' target='_blank' title='Go to RDFS to get more informations about rdfs:comment'>rdfs:comment</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasOBONamespace' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasOBONamespace'>OIO:hasOBONamespace</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#default-namespace' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:default-namespace'>OIO:default-namespace</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.w3.org/2000/01/rdf-schema#label' target='_blank' title='Go to RDFS to get more informations about rdfs:label'>rdfs:label</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasDbXref' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasDbXref'>OIO:hasDbXref</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#date' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:date'>OIO:date</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#created_by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:created_by'>OIO:created_by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#auto-generated-by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:auto-generated-by'>OIO:auto-generated-by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li></ul><h4 style=\"margin: 1em 0 0 0;\">Node tuples</h4><p class=\"paragraph\">A node tuple is a connected component composed of two nodes. We have detected 4 node tuples in the graph, involving a total of 8 nodes (10.39%) and 4 edges (1.34%). The detected node tuples are:</p><ul class=\"medium-columns\"><li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs.owl' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs.owl'>OBO:zfs.owl</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/zfs/releases/2020-03-10/zfs.owl' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs&#x2f;releases&#x2f;2020-03-10&#x2f;zfs.owl'>OBO:zfs&#x2f;releases&#x2f;2020-03-10&#x2f;zfs.owl</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs#granular_stage' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs#granular_stage'>OBO:zfs#granular_stage</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://www.geneontology.org/formats/oboInOwl#SubsetProperty' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:SubsetProperty'>OIO:SubsetProperty</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs#PLURAL' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs#PLURAL'>OBO:zfs#PLURAL</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://www.geneontology.org/formats/oboInOwl#SynonymTypeProperty' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:SynonymTypeProperty'>OIO:SynonymTypeProperty</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/RO_0002087' target='_blank' title='Go to Relation Ontology to get more informations about RO:0002087'>RO:0002087</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/BFO_0000062' target='_blank' title='Go to Basic Formal Ontology to get more informations about BFO:0000062'>BFO:0000062</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li></ul><h4 style=\"margin: 1em 0 0 0;\">Tendrils</h4><p class=\"paragraph\">A tendril is a path starting from a node of degree one, connected to a strongly connected component. We have detected  a single tendril in the graph.</p><ul class=\"large-columns\"><li><p class=\"paragraph\">Tendril starting from the root node <a href='http://purl.obolibrary.org/obo/ZFS_0100000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0100000'>OBO:ZFS_0100000</a> (degree 53), and containing a single other node, <a href='http://purl.obolibrary.org/obo/ZFS_0000000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000000'>OBO:ZFS_0000000</a>. Its nodes have a single node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>. Its edges have a single edge type, which is <a href='https://biolink.github.io/biolink-model/docs/subclass_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:subclass_of'>biolink:subclass_of</a>.</p></li></ul></div>"
      ],
      "text/plain": [
       "<div class=\"graph-report\"><style>.graph-report li {margin: 0.5em 0 0.5em 0;}.graph-report .paragraph {text-align: justify;word-break: break-all;}.graph-report .small-columns {column-count: 4;column-gap: 2em;}.graph-report .medium-columns {column-count: 3;column-gap: 2em;}.graph-report .large-columns {column-count: 2;column-gap: 2em;}.graph-report .single-column {}@media only screen and (max-width: 600px) {.graph-report .small-columns {column-count: 1;}.graph-report .medium-columns {column-count: 1;}.graph-report .large-columns {column-count: 1;}}@media only screen and (min-width: 600px) and (max-width: 800px) {.graph-report .small-columns {column-count: 2;}.graph-report .medium-columns {column-count: 1;}.graph-report .large-columns {column-count: 1;}}@media only screen and (min-width: 800px) and (max-width: 1024px) {.graph-report .small-columns {column-count: 3;}.graph-report .medium-columns {column-count: 2;}.graph-report .large-columns {column-count: 1;}}</style><h2>ZFS</h2><p class=\"paragraph\">The undirected graph ZFS has 77 homogeneous nodes and 149 heterogeneous edges. The graph contains 20 connected components (of which 15 are disconnected nodes), with the largest one containing 54 nodes and the smallest one containing a single node. The RAM requirements for the nodes and edges data structures are 9.05KB and 536B respectively.</p><h3 style=\"margin: 1em 0 0 0;\">Degree centrality</h3><p class=\"paragraph\">The minimum node degree is 0, the maximum node degree is 53, the mode degree is 4, the mean degree is 3.87 and the node degree median is 4.</p><p class=\"paragraph\">The nodes with the highest degree centrality are <a href='http://purl.obolibrary.org/obo/ZFS_0100000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0100000'>OBO:ZFS_0100000</a> (degree 53 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000045' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000045'>OBO:ZFS_0000045</a> (degree 12 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000048' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000048'>OBO:ZFS_0000048</a> (degree 10 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>), <a href='http://purl.obolibrary.org/obo/ZFS_0000049' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000049'>OBO:ZFS_0000049</a> (degree 9 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/ZFS_0000047' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000047'>OBO:ZFS_0000047</a> (degree 9 and node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p><h3 style=\"margin: 1em 0 0 0;\">Node types</h3><p class=\"paragraph\">The graph has a single node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>. The RAM requirement for the node types data structure is 4.31KB.</p><h4 style=\"margin: 1em 0 0 0;\">Homogeneous node types</h4><p class=\"paragraph\">Homogeneous node types are node types that are assigned to all the nodes in the graph, making the node type relatively meaningless, as it adds no more information than the fact that the node is in the graph. The graph contains a homogeneous node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>.</p><h3 style=\"margin: 1em 0 0 0;\">Edge types</h3><p class=\"paragraph\">The graph has 4 edge types, which are <a href='https://biolink.github.io/biolink-model/docs/subclass_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:subclass_of'>biolink:subclass_of</a> (112 edges, 37.58%), <a href='https://biolink.github.io/biolink-model/docs/preceded_by.html' target='_blank' title='Go to BioLink to get more informations about biolink:preceded_by'>biolink:preceded_by</a> (100 edges, 33.56%), <a href='https://biolink.github.io/biolink-model/docs/part_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:part_of'>biolink:part_of</a> (84 edges, 28.19%) and owl:versionIRI (2 edges, 0.67%). The RAM requirement for the edge types data structure is 1.85KB.</p><h3 style=\"margin: 1em 0 0 0;\">Topological Oddities</h3><p class=\"paragraph\">A topological oddity is a set of nodes in the graph that <i>may be derived</i> by an error during the generation of the edge list of the graph and, depending on the task, could bias the results of topology-based models. In the following paragraph, we will describe the detected topological oddities.</p><h4 style=\"margin: 1em 0 0 0;\">Singleton nodes</h4><p class=\"paragraph\">A singleton node is a node disconnected from all other nodes. We have detected 15 singleton nodes in the graph, involving a total of 15 nodes (19.48%). The detected singleton nodes are:</p><ul class=\"small-columns\"><li><p class=\"paragraph\"><a href='http://purl.obolibrary.org/obo/BFO_0000050' target='_blank' title='Go to Basic Formal Ontology to get more informations about BFO:0000050'>BFO:0000050</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasOBOFormatVersion' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasOBOFormatVersion'>OIO:hasOBOFormatVersion</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#creation_date' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:creation_date'>OIO:creation_date</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#shorthand' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:shorthand'>OIO:shorthand</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://purl.obolibrary.org/obo/IAO_0000115' target='_blank' title='Go to Information Artifact Ontology to get more informations about IAO:0000115'>IAO:0000115</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#saved-by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:saved-by'>OIO:saved-by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#id' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:id'>OIO:id</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.w3.org/2000/01/rdf-schema#comment' target='_blank' title='Go to RDFS to get more informations about rdfs:comment'>rdfs:comment</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasOBONamespace' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasOBONamespace'>OIO:hasOBONamespace</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#default-namespace' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:default-namespace'>OIO:default-namespace</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.w3.org/2000/01/rdf-schema#label' target='_blank' title='Go to RDFS to get more informations about rdfs:label'>rdfs:label</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#hasDbXref' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:hasDbXref'>OIO:hasDbXref</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#date' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:date'>OIO:date</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#created_by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:created_by'>OIO:created_by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li>\n",
       "<li><p class=\"paragraph\"><a href='http://www.geneontology.org/formats/oboInOwl#auto-generated-by' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:auto-generated-by'>OIO:auto-generated-by</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>)</p></li></ul><h4 style=\"margin: 1em 0 0 0;\">Node tuples</h4><p class=\"paragraph\">A node tuple is a connected component composed of two nodes. We have detected 4 node tuples in the graph, involving a total of 8 nodes (10.39%) and 4 edges (1.34%). The detected node tuples are:</p><ul class=\"medium-columns\"><li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs.owl' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs.owl'>OBO:zfs.owl</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/zfs/releases/2020-03-10/zfs.owl' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs&#x2f;releases&#x2f;2020-03-10&#x2f;zfs.owl'>OBO:zfs&#x2f;releases&#x2f;2020-03-10&#x2f;zfs.owl</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs#granular_stage' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs#granular_stage'>OBO:zfs#granular_stage</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://www.geneontology.org/formats/oboInOwl#SubsetProperty' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:SubsetProperty'>OIO:SubsetProperty</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/zfs#PLURAL' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:zfs#PLURAL'>OBO:zfs#PLURAL</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://www.geneontology.org/formats/oboInOwl#SynonymTypeProperty' target='_blank' title='Go to Gene Ontology OBO in OWL to get more informations about OIO:SynonymTypeProperty'>OIO:SynonymTypeProperty</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li>\n",
       "<li><p class=\"paragraph\">Node tuple containing the nodes <a href='http://purl.obolibrary.org/obo/RO_0002087' target='_blank' title='Go to Relation Ontology to get more informations about RO:0002087'>RO:0002087</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>) and <a href='http://purl.obolibrary.org/obo/BFO_0000062' target='_blank' title='Go to Basic Formal Ontology to get more informations about BFO:0000062'>BFO:0000062</a> (node type <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>).</p></li></ul><h4 style=\"margin: 1em 0 0 0;\">Tendrils</h4><p class=\"paragraph\">A tendril is a path starting from a node of degree one, connected to a strongly connected component. We have detected  a single tendril in the graph.</p><ul class=\"large-columns\"><li><p class=\"paragraph\">Tendril starting from the root node <a href='http://purl.obolibrary.org/obo/ZFS_0100000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0100000'>OBO:ZFS_0100000</a> (degree 53), and containing a single other node, <a href='http://purl.obolibrary.org/obo/ZFS_0000000' target='_blank' title='Go to Open Biomedical Ontology to get more informations about OBO:ZFS_0000000'>OBO:ZFS_0000000</a>. Its nodes have a single node type, which is <a href='https://biolink.github.io/biolink-model/docs/NamedThing.html' target='_blank' title='Go to BioLink to get more informations about biolink:NamedThing'>biolink:NamedThing</a>. Its edges have a single edge type, which is <a href='https://biolink.github.io/biolink-model/docs/subclass_of.html' target='_blank' title='Go to BioLink to get more informations about biolink:subclass_of'>biolink:subclass_of</a>.</p></li></ul></div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2ynJqB9wOPd"
   },
   "source": [
    "## Some example operations on the graph\n",
    "Here follows some examples of operations that can be executed on the graph object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q6WFsr0L1nOB"
   },
   "source": [
    "### How to easily find a method\n",
    "The library includes a user recommender system to help you find any method, for instance suppose you want to compute the diameter and do not know the method name, or maybe are distracted and mistype it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "1y8quib_1mZk",
    "outputId": "4cb46d36-0662-456c-e73d-858d91e23cb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The method 'djametr' does not exists, did you mean one of the following?\n",
      "* 'get_diameter'\n",
      "* 'get_diameter_naive'\n",
      "* 'get_isomorphic_node_ids_groups'\n",
      "* 'get_isomorphic_node_names_groups'\n",
      "* 'get_number_of_isomorphic_node_groups'\n",
      "* 'get_isomorphic_node_type_ids_groups'\n",
      "* 'get_isomorphic_node_type_names_groups'\n",
      "* 'get_number_of_isomorphic_node_type_groups'\n",
      "* 'get_approximated_isomorphic_node_type_ids_groups'\n",
      "* 'get_approximated_isomorphic_node_type_names_groups'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    graph.djametr\n",
    "except AttributeError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GCeaS8DU16s2"
   },
   "source": [
    "Additionally, generally speaking you can alway use the `dir` method to search for a method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-14htFlk2D7x",
    "outputId": "f3439e05-f86f-46ab-fb65-0c93dbfba02f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__and__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__rand__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__ror__',\n",
       " '__rsub__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__xor__',\n",
       " '_repr_html_',\n",
       " 'add_edge_type_name_inplace',\n",
       " 'add_node_type_id_from_node_name_prefixes',\n",
       " 'add_node_type_id_from_node_name_prefixes_inplace',\n",
       " 'add_node_type_name_from_node_name_prefixes',\n",
       " 'add_node_type_name_from_node_name_prefixes_inplace',\n",
       " 'add_node_type_name_inplace',\n",
       " 'add_selfloops',\n",
       " 'approximated_vertex_cover_set',\n",
       " 'are_nodes_remappable',\n",
       " 'build_bipartite_graph_from_edge_node_ids',\n",
       " 'build_bipartite_graph_from_edge_node_names',\n",
       " 'build_bipartite_graph_from_edge_node_prefixes',\n",
       " 'build_bipartite_graph_from_edge_node_types',\n",
       " 'build_clique_graph_from_node_ids',\n",
       " 'build_clique_graph_from_node_names',\n",
       " 'build_clique_graph_from_node_prefixes',\n",
       " 'build_clique_graph_from_node_types',\n",
       " 'complete_walks',\n",
       " 'connected_holdout',\n",
       " 'contains',\n",
       " 'contains_identity_matrix',\n",
       " 'decode_edge',\n",
       " 'disable_all',\n",
       " 'divide_edge_weights',\n",
       " 'divide_edge_weights_inplace',\n",
       " 'dump_edges',\n",
       " 'dump_nodes',\n",
       " 'enable',\n",
       " 'encode_edge',\n",
       " 'filter_from_ids',\n",
       " 'filter_from_names',\n",
       " 'from_csv',\n",
       " 'generate_barbell_graph',\n",
       " 'generate_chain_graph',\n",
       " 'generate_circle_graph',\n",
       " 'generate_complete_graph',\n",
       " 'generate_lollipop_graph',\n",
       " 'generate_new_edges_from_node_features',\n",
       " 'generate_random_connected_graph',\n",
       " 'generate_random_spanning_tree',\n",
       " 'generate_squared_lattice_graph',\n",
       " 'generate_star_graph',\n",
       " 'generate_wheel_graph',\n",
       " 'get_adamic_adar_coo_matrix',\n",
       " 'get_adamic_adar_graph',\n",
       " 'get_adamic_adar_index_from_node_ids',\n",
       " 'get_adamic_adar_index_from_node_names',\n",
       " 'get_adamic_adar_scores',\n",
       " 'get_all_edge_metrics',\n",
       " 'get_all_edge_metrics_from_node_ids',\n",
       " 'get_all_edge_metrics_from_node_ids_tuple',\n",
       " 'get_all_shortest_paths',\n",
       " 'get_ancestors_jaccard_coo_matrix',\n",
       " 'get_ancestors_jaccard_graph',\n",
       " 'get_approximated_betweenness_centrality_from_node_id',\n",
       " 'get_approximated_betweenness_centrality_from_node_name',\n",
       " 'get_approximated_cliques',\n",
       " 'get_approximated_isomorphic_node_type_ids_groups',\n",
       " 'get_approximated_isomorphic_node_type_names_groups',\n",
       " 'get_approximated_number_of_cliques',\n",
       " 'get_available_edge_metrics_names',\n",
       " 'get_average_clustering_coefficient',\n",
       " 'get_betweenness_centrality',\n",
       " 'get_bfs_topological_sorting_from_node_id',\n",
       " 'get_bipartite_edge_names',\n",
       " 'get_bipartite_edges',\n",
       " 'get_boolean_node_type_ids',\n",
       " 'get_breadth_first_search_from_node_ids',\n",
       " 'get_breadth_first_search_from_node_names',\n",
       " 'get_breadth_first_search_random_nodes',\n",
       " 'get_chains',\n",
       " 'get_circles',\n",
       " 'get_clique_edge_names',\n",
       " 'get_clique_edges',\n",
       " 'get_closeness_centrality',\n",
       " 'get_clustering_coefficient',\n",
       " 'get_clustering_coefficient_per_node',\n",
       " 'get_comulative_node_degree_from_node_id',\n",
       " 'get_connected_components',\n",
       " 'get_cooccurrence_coo_matrix',\n",
       " 'get_cooccurrence_graph',\n",
       " 'get_cumulative_node_degrees',\n",
       " 'get_degree_centrality',\n",
       " 'get_dendritic_trees',\n",
       " 'get_dense_binary_adjacency_matrix',\n",
       " 'get_dense_modularity_matrix',\n",
       " 'get_dense_nodes_mapping',\n",
       " 'get_dense_weighted_adjacency_matrix',\n",
       " 'get_density',\n",
       " 'get_destination_names',\n",
       " 'get_destination_node_id_from_edge_id',\n",
       " 'get_destination_node_ids',\n",
       " 'get_destination_node_name_from_edge_id',\n",
       " 'get_diameter',\n",
       " 'get_diameter_naive',\n",
       " 'get_dijkstra_from_node_ids',\n",
       " 'get_dijkstra_from_node_names',\n",
       " 'get_directed_destination_node_ids',\n",
       " 'get_directed_destination_nodes_with_known_edge_types',\n",
       " 'get_directed_edge_ids_from_edge_type_id',\n",
       " 'get_directed_edge_ids_from_edge_type_name',\n",
       " 'get_directed_edge_ids_from_node_curie_prefixes',\n",
       " 'get_directed_edge_node_ids',\n",
       " 'get_directed_edge_node_ids_from_edge_type_id',\n",
       " 'get_directed_edge_node_ids_from_edge_type_name',\n",
       " 'get_directed_edge_node_ids_from_node_curie_prefixes',\n",
       " 'get_directed_edge_node_names',\n",
       " 'get_directed_edge_node_names_from_edge_type_id',\n",
       " 'get_directed_edge_node_names_from_edge_type_name',\n",
       " 'get_directed_edge_node_names_from_node_curie_prefixes',\n",
       " 'get_directed_edge_triples_ids',\n",
       " 'get_directed_edge_triples_names',\n",
       " 'get_directed_edge_type_ids',\n",
       " 'get_directed_edge_weights',\n",
       " 'get_directed_modularity_from_node_community_memberships',\n",
       " 'get_directed_source_node_ids',\n",
       " 'get_directed_source_nodes_with_known_edge_types',\n",
       " 'get_eccentricity_and_most_distant_node_id_from_node_id',\n",
       " 'get_eccentricity_from_node_name',\n",
       " 'get_edge_count_from_edge_type_id',\n",
       " 'get_edge_count_from_edge_type_name',\n",
       " 'get_edge_id_from_node_ids',\n",
       " 'get_edge_id_from_node_ids_and_edge_type_id',\n",
       " 'get_edge_id_from_node_names',\n",
       " 'get_edge_id_from_node_names_and_edge_type_name',\n",
       " 'get_edge_ids_from_node_ids',\n",
       " 'get_edge_ids_with_known_edge_types',\n",
       " 'get_edge_ids_with_unknown_edge_types',\n",
       " 'get_edge_label_holdout_graphs',\n",
       " 'get_edge_label_kfold',\n",
       " 'get_edge_label_random_holdout',\n",
       " 'get_edge_node_ids',\n",
       " 'get_edge_node_ids_from_edge_node_names',\n",
       " 'get_edge_node_ids_from_edge_type_id',\n",
       " 'get_edge_node_ids_from_edge_type_name',\n",
       " 'get_edge_node_ids_with_known_edge_types',\n",
       " 'get_edge_node_ids_with_unknown_edge_types',\n",
       " 'get_edge_node_names',\n",
       " 'get_edge_node_names_from_edge_node_ids',\n",
       " 'get_edge_node_names_with_known_edge_types',\n",
       " 'get_edge_node_names_with_unknown_edge_types',\n",
       " 'get_edge_prediction_chunk_mini_batch',\n",
       " 'get_edge_prediction_kfold',\n",
       " 'get_edge_prediction_mini_batch',\n",
       " 'get_edge_prediction_subgraphs',\n",
       " 'get_edge_prediction_subgraphs_from_node_ids',\n",
       " 'get_edge_type_id_counts_hashmap',\n",
       " 'get_edge_type_id_from_edge_id',\n",
       " 'get_edge_type_id_from_edge_node_ids',\n",
       " 'get_edge_type_id_from_edge_type_name',\n",
       " 'get_edge_type_ids_from_edge_type_names',\n",
       " 'get_edge_type_name_from_edge_id',\n",
       " 'get_edge_type_name_from_edge_type_id',\n",
       " 'get_edge_type_names',\n",
       " 'get_edge_type_names_counts_hashmap',\n",
       " 'get_edge_types_total_memory_requirements',\n",
       " 'get_edge_types_total_memory_requirements_human_readable',\n",
       " 'get_edge_weight_from_edge_id',\n",
       " 'get_edge_weight_from_node_ids',\n",
       " 'get_edge_weight_from_node_ids_and_edge_type_id',\n",
       " 'get_edge_weight_from_node_names',\n",
       " 'get_edge_weight_from_node_names_and_edge_type_name',\n",
       " 'get_edge_weighting_methods',\n",
       " 'get_edge_weights_total_memory_requirements',\n",
       " 'get_edge_weights_total_memory_requirements_human_readable',\n",
       " 'get_edges_total_memory_requirement',\n",
       " 'get_edges_total_memory_requirement_human_readable',\n",
       " 'get_edges_with_known_edge_types_mask',\n",
       " 'get_edges_with_unknown_edge_types_mask',\n",
       " 'get_eigenvector_centrality',\n",
       " 'get_four_sweep',\n",
       " 'get_harmonic_centrality',\n",
       " 'get_homogeneous_node_type_ids',\n",
       " 'get_homogeneous_node_type_names',\n",
       " 'get_isomorphic_edge_type_ids_groups',\n",
       " 'get_isomorphic_edge_type_names_groups',\n",
       " 'get_isomorphic_node_ids_groups',\n",
       " 'get_isomorphic_node_names_groups',\n",
       " 'get_isomorphic_node_type_ids_groups',\n",
       " 'get_isomorphic_node_type_names_groups',\n",
       " 'get_jaccard_coefficient_from_node_ids',\n",
       " 'get_jaccard_coefficient_from_node_names',\n",
       " 'get_jaccard_coefficient_scores',\n",
       " 'get_jaccard_coo_matrix',\n",
       " 'get_jaccard_graph',\n",
       " 'get_k_shortest_path_node_ids_from_node_ids',\n",
       " 'get_k_shortest_path_node_ids_from_node_names',\n",
       " 'get_k_shortest_path_node_names_from_node_names',\n",
       " 'get_known_boolean_node_type_ids',\n",
       " 'get_known_edge_type_ids',\n",
       " 'get_known_edge_types_mask',\n",
       " 'get_known_edge_types_rate',\n",
       " 'get_known_node_types_mask',\n",
       " 'get_known_node_types_rate',\n",
       " 'get_known_single_label_node_type_ids',\n",
       " 'get_laplacian_coo_matrix',\n",
       " 'get_laplacian_graph',\n",
       " 'get_left_normalized_laplacian_coo_matrix',\n",
       " 'get_left_normalized_laplacian_graph',\n",
       " 'get_log_normalized_cooccurrence_coo_matrix',\n",
       " 'get_log_normalized_cooccurrence_graph',\n",
       " 'get_max_clique',\n",
       " 'get_max_encodable_edge_number',\n",
       " 'get_maximum_edge_weight',\n",
       " 'get_maximum_multilabel_count',\n",
       " 'get_maximum_node_degree',\n",
       " 'get_maximum_node_types_number',\n",
       " 'get_memory_stats',\n",
       " 'get_minimum_edge_types_number',\n",
       " 'get_minimum_node_degree',\n",
       " 'get_minimum_node_types_number',\n",
       " 'get_mininum_edge_weight',\n",
       " 'get_minmax_edge_ids_from_node_ids',\n",
       " 'get_minmax_edge_ids_from_source_node_id',\n",
       " 'get_most_central_node_id',\n",
       " 'get_multigraph_edge_ids_from_node_ids',\n",
       " 'get_multiple_node_type_ids_from_node_type_names',\n",
       " 'get_name',\n",
       " 'get_neighbour_node_ids_from_node_id',\n",
       " 'get_neighbour_node_ids_from_node_name',\n",
       " 'get_neighbour_node_names_from_node_name',\n",
       " 'get_neighbours_intersection_size_coo_matrix',\n",
       " 'get_neighbours_intersection_size_graph',\n",
       " 'get_node_connected_component_ids',\n",
       " 'get_node_count_from_node_type_id',\n",
       " 'get_node_count_from_node_type_name',\n",
       " 'get_node_degree_from_node_id',\n",
       " 'get_node_degree_from_node_name',\n",
       " 'get_node_degree_geometric_distribution_threshold',\n",
       " 'get_node_degrees',\n",
       " 'get_node_degrees_mean',\n",
       " 'get_node_degrees_median',\n",
       " 'get_node_degrees_mode',\n",
       " 'get_node_id_from_node_name',\n",
       " 'get_node_ids',\n",
       " 'get_node_ids_and_edge_type_id_and_edge_weight_from_edge_id',\n",
       " 'get_node_ids_and_edge_type_id_from_edge_id',\n",
       " 'get_node_ids_from_edge_id',\n",
       " 'get_node_ids_from_node_curie_prefixes',\n",
       " 'get_node_ids_from_node_names',\n",
       " 'get_node_ids_from_node_type_id',\n",
       " 'get_node_ids_from_node_type_ids',\n",
       " 'get_node_ids_from_node_type_name',\n",
       " 'get_node_ids_from_node_type_names',\n",
       " 'get_node_ids_mapping_from_graph',\n",
       " 'get_node_ids_with_known_node_types',\n",
       " 'get_node_ids_with_unknown_node_types',\n",
       " 'get_node_indegrees',\n",
       " 'get_node_label_holdout_graphs',\n",
       " 'get_node_label_holdout_indices',\n",
       " 'get_node_label_holdout_labels',\n",
       " 'get_node_label_kfold',\n",
       " 'get_node_label_random_holdout',\n",
       " 'get_node_name_from_node_id',\n",
       " 'get_node_names',\n",
       " 'get_node_names_from_edge_id',\n",
       " 'get_node_names_from_node_curie_prefixes',\n",
       " 'get_node_names_from_node_type_id',\n",
       " 'get_node_names_from_node_type_name',\n",
       " 'get_node_names_prefixes',\n",
       " 'get_node_names_with_known_node_types',\n",
       " 'get_node_names_with_unknown_node_types',\n",
       " 'get_node_ontologies',\n",
       " 'get_node_report_from_node_id',\n",
       " 'get_node_report_from_node_name',\n",
       " 'get_node_sampling_methods',\n",
       " 'get_node_tuples',\n",
       " 'get_node_type_id_counts_hashmap',\n",
       " 'get_node_type_id_from_node_type_name',\n",
       " 'get_node_type_ids',\n",
       " 'get_node_type_ids_from_node_id',\n",
       " 'get_node_type_ids_from_node_name',\n",
       " 'get_node_type_ids_from_node_type_names',\n",
       " 'get_node_type_name_from_node_name',\n",
       " 'get_node_type_name_from_node_type_id',\n",
       " 'get_node_type_names',\n",
       " 'get_node_type_names_counts_hashmap',\n",
       " 'get_node_type_names_from_node_id',\n",
       " 'get_node_type_names_from_node_name',\n",
       " 'get_node_types_total_memory_requirements',\n",
       " 'get_node_types_total_memory_requirements_human_readable',\n",
       " 'get_node_urls',\n",
       " 'get_nodes_mapping',\n",
       " 'get_nodes_total_memory_requirement',\n",
       " 'get_nodes_total_memory_requirement_human_readable',\n",
       " 'get_nodes_with_known_node_types_mask',\n",
       " 'get_nodes_with_unknown_node_types_mask',\n",
       " 'get_non_zero_subgraph_node_degrees',\n",
       " 'get_normalized_cooccurrence_coo_matrix',\n",
       " 'get_normalized_cooccurrence_graph',\n",
       " 'get_not_singletons_node_ids',\n",
       " 'get_number_of_approximated_isomorphic_node_type_groups',\n",
       " 'get_number_of_available_edge_metrics',\n",
       " 'get_number_of_connected_components',\n",
       " 'get_number_of_connected_nodes',\n",
       " 'get_number_of_directed_edges',\n",
       " 'get_number_of_directed_edges_from_node_curie_prefixes',\n",
       " 'get_number_of_disconnected_nodes',\n",
       " 'get_number_of_edge_types',\n",
       " 'get_number_of_edges',\n",
       " 'get_number_of_edges_from_edge_type_id',\n",
       " 'get_number_of_edges_from_edge_type_name',\n",
       " 'get_number_of_homogeneous_node_types',\n",
       " 'get_number_of_isomorphic_edge_type_groups',\n",
       " 'get_number_of_isomorphic_node_groups',\n",
       " 'get_number_of_isomorphic_node_type_groups',\n",
       " 'get_number_of_known_edge_types',\n",
       " 'get_number_of_known_node_types',\n",
       " 'get_number_of_multigraph_edges_from_node_ids',\n",
       " 'get_number_of_node_types',\n",
       " 'get_number_of_nodes',\n",
       " 'get_number_of_nodes_from_node_curie_prefixes',\n",
       " 'get_number_of_nodes_from_node_type_id',\n",
       " 'get_number_of_nodes_from_node_type_name',\n",
       " 'get_number_of_parallel_edges',\n",
       " 'get_number_of_selfloops',\n",
       " 'get_number_of_singleton_edge_types',\n",
       " 'get_number_of_singleton_node_types',\n",
       " 'get_number_of_singleton_nodes',\n",
       " 'get_number_of_singleton_nodes_with_selfloops',\n",
       " 'get_number_of_trap_nodes',\n",
       " 'get_number_of_triads',\n",
       " 'get_number_of_triangles',\n",
       " 'get_number_of_triangles_per_node',\n",
       " 'get_number_of_undirected_edges',\n",
       " 'get_number_of_unique_directed_edges',\n",
       " 'get_number_of_unique_edges',\n",
       " 'get_number_of_unique_selfloops',\n",
       " 'get_number_of_unique_source_nodes',\n",
       " 'get_number_of_unique_undirected_edges',\n",
       " 'get_number_of_unknown_edge_types',\n",
       " 'get_number_of_unknown_node_types',\n",
       " 'get_number_of_weighted_singleton_nodes',\n",
       " 'get_number_of_weighted_triads',\n",
       " 'get_okapi_bm25_node_feature_propagation',\n",
       " 'get_okapi_bm25_node_label_propagation',\n",
       " 'get_one_hot_encoded_edge_types',\n",
       " 'get_one_hot_encoded_known_edge_types',\n",
       " 'get_one_hot_encoded_known_node_types',\n",
       " 'get_one_hot_encoded_node_types',\n",
       " 'get_ontology_from_node_id',\n",
       " 'get_ontology_from_node_name',\n",
       " 'get_preferential_attachment_from_node_ids',\n",
       " 'get_preferential_attachment_from_node_names',\n",
       " 'get_preferential_attachment_scores',\n",
       " 'get_random_edge_id',\n",
       " 'get_random_edge_type',\n",
       " 'get_random_inbounds_scale_free_node',\n",
       " 'get_random_node',\n",
       " 'get_random_node_type',\n",
       " 'get_random_outbounds_scale_free_node',\n",
       " 'get_random_subgraph',\n",
       " 'get_random_zifian_edge_type',\n",
       " 'get_reciprocal_sqrt_degree_from_node_id',\n",
       " 'get_reciprocal_sqrt_degrees',\n",
       " 'get_resource_allocation_index_from_node_ids',\n",
       " 'get_resource_allocation_index_from_node_names',\n",
       " 'get_resource_allocation_index_scores',\n",
       " 'get_reversed_bfs_topological_sorting_from_node_id',\n",
       " 'get_right_normalized_laplacian_coo_matrix',\n",
       " 'get_right_normalized_laplacian_graph',\n",
       " 'get_root_node_ids',\n",
       " 'get_root_node_names',\n",
       " 'get_selfloop_nodes_rate',\n",
       " 'get_shared_ancestors_jaccard_adjacency_matrix',\n",
       " 'get_shared_ancestors_size_adjacency_matrix',\n",
       " 'get_shared_ancestors_size_coo_matrix',\n",
       " 'get_shared_ancestors_size_graph',\n",
       " 'get_shortest_path_node_ids_from_node_ids',\n",
       " 'get_shortest_path_node_ids_from_node_names',\n",
       " 'get_shortest_path_node_names_from_node_names',\n",
       " 'get_shortest_paths_matrix',\n",
       " 'get_siamese_mini_batch',\n",
       " 'get_siamese_mini_batch_with_edge_types',\n",
       " 'get_single_label_node_type_ids',\n",
       " 'get_singleton_edge_type_ids',\n",
       " 'get_singleton_edge_type_names',\n",
       " 'get_singleton_node_ids',\n",
       " 'get_singleton_node_names',\n",
       " 'get_singleton_node_type_ids',\n",
       " 'get_singleton_node_type_names',\n",
       " 'get_singleton_with_selfloops_node_ids',\n",
       " 'get_singleton_with_selfloops_node_names',\n",
       " 'get_sorted_unique_random_nodes',\n",
       " 'get_source_names',\n",
       " 'get_source_node_id_from_edge_id',\n",
       " 'get_source_node_ids',\n",
       " 'get_source_node_name_from_edge_id',\n",
       " 'get_sparse_edge_weighting_methods',\n",
       " 'get_sparse_subgraphs',\n",
       " 'get_sparse_undirected_laplacian_subgraphs',\n",
       " 'get_star_edge_names',\n",
       " 'get_star_edges',\n",
       " 'get_stars',\n",
       " 'get_stress_centrality',\n",
       " 'get_subgraphs',\n",
       " 'get_subsampled_nodes',\n",
       " 'get_symmetric_normalized_laplacian_coo_matrix',\n",
       " 'get_symmetric_normalized_laplacian_graph',\n",
       " 'get_tendrils',\n",
       " 'get_top_k_central_node_ids',\n",
       " 'get_top_k_central_node_names',\n",
       " 'get_total_edge_weights',\n",
       " 'get_total_memory_used',\n",
       " 'get_transitive_closure',\n",
       " 'get_transitivity',\n",
       " 'get_trap_node_ids',\n",
       " 'get_trap_nodes_rate',\n",
       " 'get_unchecked_adamic_adar_index_from_node_ids',\n",
       " 'get_unchecked_all_edge_metrics_from_node_ids_tuple',\n",
       " 'get_unchecked_breadth_first_search_distances_parallel_from_node_id',\n",
       " 'get_unchecked_breadth_first_search_distances_parallel_from_node_ids',\n",
       " 'get_unchecked_breadth_first_search_distances_sequential_from_node_id',\n",
       " 'get_unchecked_breadth_first_search_from_node_id',\n",
       " 'get_unchecked_breadth_first_search_from_node_ids',\n",
       " 'get_unchecked_breadth_first_search_predecessors_parallel_from_node_id',\n",
       " 'get_unchecked_closeness_centrality_from_node_id',\n",
       " 'get_unchecked_comulative_node_degree_from_node_id',\n",
       " 'get_unchecked_destination_node_id_from_edge_id',\n",
       " 'get_unchecked_destination_node_name_from_edge_id',\n",
       " 'get_unchecked_dijkstra_from_node_id',\n",
       " 'get_unchecked_dijkstra_from_node_ids',\n",
       " 'get_unchecked_eccentricity_and_most_distant_node_id_from_node_id',\n",
       " 'get_unchecked_edge_count_from_edge_type_id',\n",
       " 'get_unchecked_edge_id_from_node_ids',\n",
       " 'get_unchecked_edge_id_from_node_ids_and_edge_type_id',\n",
       " 'get_unchecked_edge_type_id_counts_hashmap_from_node_ids',\n",
       " 'get_unchecked_edge_type_id_from_edge_id',\n",
       " 'get_unchecked_edge_type_id_from_edge_type_name',\n",
       " 'get_unchecked_edge_type_name_from_edge_type_id',\n",
       " 'get_unchecked_edge_weight_from_edge_id',\n",
       " 'get_unchecked_edge_weight_from_node_ids',\n",
       " 'get_unchecked_harmonic_centrality_from_node_id',\n",
       " 'get_unchecked_jaccard_coefficient_from_node_ids',\n",
       " 'get_unchecked_k_shortest_path_node_ids_from_node_ids',\n",
       " 'get_unchecked_maximum_node_degree',\n",
       " 'get_unchecked_maximum_preferential_attachment',\n",
       " 'get_unchecked_minimum_node_degree',\n",
       " 'get_unchecked_minimum_preferential_attachment',\n",
       " 'get_unchecked_minmax_edge_ids_from_node_ids',\n",
       " 'get_unchecked_minmax_edge_ids_from_source_node_id',\n",
       " 'get_unchecked_most_central_node_id',\n",
       " 'get_unchecked_neighbours_intersection_size_from_node_ids',\n",
       " 'get_unchecked_node_count_from_node_type_id',\n",
       " 'get_unchecked_node_degree_from_node_id',\n",
       " 'get_unchecked_node_id_from_node_name',\n",
       " 'get_unchecked_node_ids_and_edge_type_id_and_edge_weight_from_edge_id',\n",
       " 'get_unchecked_node_ids_and_edge_type_id_from_edge_id',\n",
       " 'get_unchecked_node_ids_from_edge_id',\n",
       " 'get_unchecked_node_name_from_node_id',\n",
       " 'get_unchecked_node_names_from_edge_id',\n",
       " 'get_unchecked_node_type_id_counts_hashmap_from_node_ids',\n",
       " 'get_unchecked_node_type_ids_from_node_id',\n",
       " 'get_unchecked_node_type_names_from_node_id',\n",
       " 'get_unchecked_node_type_names_from_node_type_ids',\n",
       " 'get_unchecked_number_of_edges_from_edge_type_id',\n",
       " 'get_unchecked_number_of_nodes_from_node_type_id',\n",
       " 'get_unchecked_ontology_from_node_id',\n",
       " 'get_unchecked_ontology_from_node_name',\n",
       " 'get_unchecked_preferential_attachment_from_node_ids',\n",
       " 'get_unchecked_random_zifian_edge_type',\n",
       " 'get_unchecked_reciprocal_sqrt_degree_from_node_id',\n",
       " 'get_unchecked_reciprocal_sqrt_degrees_from_node_ids',\n",
       " 'get_unchecked_resource_allocation_index_from_node_ids',\n",
       " 'get_unchecked_shortest_path_node_ids_from_node_ids',\n",
       " 'get_unchecked_shortest_path_node_names_from_node_ids',\n",
       " 'get_unchecked_source_node_id_from_edge_id',\n",
       " 'get_unchecked_source_node_name_from_edge_id',\n",
       " 'get_unchecked_structural_distance_from_node_ids',\n",
       " 'get_unchecked_unique_source_node_id',\n",
       " 'get_unchecked_weighted_closeness_centrality_from_node_id',\n",
       " 'get_unchecked_weighted_eccentricity_from_node_id',\n",
       " 'get_unchecked_weighted_harmonic_centrality_from_node_id',\n",
       " 'get_unchecked_weighted_maximum_preferential_attachment',\n",
       " 'get_unchecked_weighted_minimum_preferential_attachment',\n",
       " 'get_unchecked_weighted_node_degree_from_node_id',\n",
       " 'get_unchecked_weighted_preferential_attachment_from_node_ids',\n",
       " 'get_unchecked_weighted_resource_allocation_index_from_node_ids',\n",
       " 'get_unchecked_weighted_shortest_path_node_ids_from_node_ids',\n",
       " 'get_unchecked_weighted_shortest_path_node_names_from_node_ids',\n",
       " 'get_undirected_edge_type_ids',\n",
       " 'get_undirected_edge_weights',\n",
       " 'get_undirected_louvain_community_detection',\n",
       " 'get_undirected_modularity_from_node_community_memberships',\n",
       " 'get_uniform_random_walk_random_nodes',\n",
       " 'get_unique_edge_type_ids',\n",
       " 'get_unique_edge_type_names',\n",
       " 'get_unique_node_type_ids',\n",
       " 'get_unique_node_type_names',\n",
       " 'get_unknown_edge_types_mask',\n",
       " 'get_unknown_edge_types_rate',\n",
       " 'get_unknown_node_types_mask',\n",
       " 'get_unknown_node_types_rate',\n",
       " 'get_weighted_all_shortest_paths',\n",
       " 'get_weighted_approximated_betweenness_centrality_from_node_id',\n",
       " 'get_weighted_approximated_betweenness_centrality_from_node_name',\n",
       " 'get_weighted_closeness_centrality',\n",
       " 'get_weighted_degree_centrality',\n",
       " 'get_weighted_eccentricity_from_node_id',\n",
       " 'get_weighted_eccentricity_from_node_name',\n",
       " 'get_weighted_eigenvector_centrality',\n",
       " 'get_weighted_harmonic_centrality',\n",
       " 'get_weighted_maximum_node_degree',\n",
       " 'get_weighted_minimum_node_degree',\n",
       " 'get_weighted_node_degree_from_node_id',\n",
       " 'get_weighted_node_degrees',\n",
       " 'get_weighted_node_degrees_mean',\n",
       " 'get_weighted_node_degrees_median',\n",
       " 'get_weighted_node_indegrees',\n",
       " 'get_weighted_preferential_attachment_from_node_ids',\n",
       " 'get_weighted_preferential_attachment_from_node_names',\n",
       " 'get_weighted_resource_allocation_index_from_node_ids',\n",
       " 'get_weighted_resource_allocation_index_from_node_names',\n",
       " 'get_weighted_shortest_path_node_ids_from_node_ids',\n",
       " 'get_weighted_shortest_path_node_ids_from_node_names',\n",
       " 'get_weighted_shortest_path_node_names_from_node_names',\n",
       " 'get_weighted_top_k_central_node_ids',\n",
       " 'has_compatible_edge_types_vocabularies',\n",
       " 'has_compatible_node_types_vocabularies',\n",
       " 'has_compatible_node_vocabularies',\n",
       " 'has_constant_edge_weights',\n",
       " 'has_cumulative_node_degrees_tradeoff_enabled',\n",
       " 'has_default_graph_name',\n",
       " 'has_destinations_tradeoff_enabled',\n",
       " 'has_disconnected_nodes',\n",
       " 'has_edge_from_node_id_and_edge_type_id',\n",
       " 'has_edge_from_node_ids',\n",
       " 'has_edge_from_node_ids_and_edge_type_id',\n",
       " 'has_edge_from_node_names',\n",
       " 'has_edge_from_node_names_and_edge_type_name',\n",
       " 'has_edge_type_id',\n",
       " 'has_edge_type_name',\n",
       " 'has_edge_types',\n",
       " 'has_edge_types_oddities',\n",
       " 'has_edge_weights',\n",
       " 'has_edge_weights_representing_probabilities',\n",
       " 'has_edges',\n",
       " 'has_exclusively_homogeneous_node_types',\n",
       " 'has_homogeneous_edge_types',\n",
       " 'has_homogeneous_node_ontologies',\n",
       " 'has_homogeneous_node_types',\n",
       " 'has_isomorphic_node_types_from_node_ids',\n",
       " 'has_isomorphic_nodes',\n",
       " 'has_known_edge_types',\n",
       " 'has_known_node_types',\n",
       " 'has_multilabel_node_types',\n",
       " 'has_negative_edge_weights',\n",
       " 'has_node_name',\n",
       " 'has_node_name_and_node_type_name',\n",
       " 'has_node_oddities',\n",
       " 'has_node_ontologies',\n",
       " 'has_node_type_id',\n",
       " 'has_node_type_name',\n",
       " 'has_node_types',\n",
       " 'has_node_types_oddities',\n",
       " 'has_nodes',\n",
       " 'has_nodes_sorted_by_decreasing_outbound_node_degree',\n",
       " 'has_nodes_sorted_by_increasing_outbound_node_degree',\n",
       " 'has_nodes_sorted_by_lexicographic_order',\n",
       " 'has_reciprocal_sqrt_degrees_tradeoff_enabled',\n",
       " 'has_same_adjacency_matrix',\n",
       " 'has_selfloop_from_node_id',\n",
       " 'has_selfloops',\n",
       " 'has_singleton_edge_types',\n",
       " 'has_singleton_node_types',\n",
       " 'has_singleton_nodes',\n",
       " 'has_singleton_nodes_with_selfloops',\n",
       " 'has_sources_tradeoff_enabled',\n",
       " 'has_trap_nodes',\n",
       " 'has_unchecked_edge_from_node_id_and_edge_type_id',\n",
       " 'has_unchecked_isomorphic_node_types_from_node_ids',\n",
       " 'has_unknown_edge_types',\n",
       " 'has_unknown_node_ontologies',\n",
       " 'has_unknown_node_types',\n",
       " 'has_weighted_singleton_nodes',\n",
       " 'hash',\n",
       " 'is_compatible',\n",
       " 'is_connected',\n",
       " 'is_connected_from_node_id',\n",
       " 'is_directed',\n",
       " 'is_directed_acyclic',\n",
       " 'is_multigraph',\n",
       " 'is_singleton_from_node_id',\n",
       " 'is_singleton_from_node_name',\n",
       " 'is_singleton_with_selfloops_from_node_id',\n",
       " 'is_trap_node_from_node_id',\n",
       " 'is_unchecked_connected_from_node_id',\n",
       " 'is_unchecked_disconnected_node_from_node_id',\n",
       " 'is_unchecked_singleton_from_node_id',\n",
       " 'is_unchecked_singleton_from_node_name',\n",
       " 'is_unchecked_singleton_with_selfloops_from_node_id',\n",
       " 'is_unchecked_trap_node_from_node_id',\n",
       " 'multiply_edge_weights',\n",
       " 'multiply_edge_weights_inplace',\n",
       " 'must_be_connected',\n",
       " 'must_be_directed_acyclic',\n",
       " 'must_be_multigraph',\n",
       " 'must_be_undirected',\n",
       " 'must_contain_identity_matrix',\n",
       " 'must_have_edges',\n",
       " 'must_have_node_ontologies',\n",
       " 'must_have_nodes',\n",
       " 'must_not_be_multigraph',\n",
       " 'must_not_contain_unknown_edge_types',\n",
       " 'must_not_contain_unknown_node_types',\n",
       " 'must_not_contain_weighted_singleton_nodes',\n",
       " 'must_not_have_trap_nodes',\n",
       " 'must_share_node_vocabulary',\n",
       " 'node2vec',\n",
       " 'normalize_edge_weights',\n",
       " 'normalize_edge_weights_inplace',\n",
       " 'overlap_textual_report',\n",
       " 'overlaps',\n",
       " 'random_holdout',\n",
       " 'random_spanning_arborescence_kruskal',\n",
       " 'random_walks',\n",
       " 'remap_from_graph',\n",
       " 'remap_from_node_ids',\n",
       " 'remap_from_node_names',\n",
       " 'remap_from_node_names_map',\n",
       " 'remap_unchecked_from_node_ids',\n",
       " 'remove_components',\n",
       " 'remove_dendritic_trees',\n",
       " 'remove_disconnected_nodes',\n",
       " 'remove_edge_type_id',\n",
       " 'remove_edge_type_name',\n",
       " 'remove_edge_types',\n",
       " 'remove_edge_weights',\n",
       " 'remove_homogeneous_node_types',\n",
       " 'remove_inplace_edge_type_ids',\n",
       " 'remove_inplace_edge_type_name',\n",
       " 'remove_inplace_edge_types',\n",
       " 'remove_inplace_edge_weights',\n",
       " 'remove_inplace_homogeneous_node_types',\n",
       " 'remove_inplace_isomorphic_edge_types',\n",
       " 'remove_inplace_isomorphic_node_types',\n",
       " 'remove_inplace_node_type_ids',\n",
       " 'remove_inplace_node_type_name',\n",
       " 'remove_inplace_node_type_names',\n",
       " 'remove_inplace_node_types',\n",
       " 'remove_inplace_singleton_edge_types',\n",
       " 'remove_inplace_singleton_node_types',\n",
       " 'remove_isomorphic_edge_types',\n",
       " 'remove_isomorphic_node_types',\n",
       " 'remove_isomorphic_nodes',\n",
       " 'remove_node_type_id',\n",
       " 'remove_node_type_name',\n",
       " 'remove_node_type_names',\n",
       " 'remove_node_types',\n",
       " 'remove_parallel_edges',\n",
       " 'remove_selfloops',\n",
       " 'remove_singleton_edge_types',\n",
       " 'remove_singleton_node_types',\n",
       " 'remove_singleton_nodes',\n",
       " 'remove_singleton_nodes_with_selfloops',\n",
       " 'remove_tendrils',\n",
       " 'remove_unknown_edge_types',\n",
       " 'remove_unknown_node_types',\n",
       " 'replace_edge_type_id_from_edge_node_type_ids',\n",
       " 'replace_edge_type_id_from_edge_node_type_ids_inplace',\n",
       " 'replace_edge_type_name_from_edge_node_type_names',\n",
       " 'replace_edge_type_name_from_edge_node_type_names_inplace',\n",
       " 'report',\n",
       " 'sample_negative_graph',\n",
       " 'sample_positive_graph',\n",
       " 'set_all_edge_types',\n",
       " 'set_all_node_types',\n",
       " 'set_inplace_all_edge_types',\n",
       " 'set_inplace_all_node_types',\n",
       " 'set_name',\n",
       " 'sort_by_bfs_topological_sorting_from_node_id',\n",
       " 'sort_by_decreasing_outbound_node_degree',\n",
       " 'sort_by_increasing_outbound_node_degree',\n",
       " 'sort_by_node_lexicographic_order',\n",
       " 'spanning_arborescence',\n",
       " 'spanning_arborescence_kruskal',\n",
       " 'strongly_connected_components',\n",
       " 'textual_report',\n",
       " 'to_anti_diagonal',\n",
       " 'to_arrowhead',\n",
       " 'to_bidiagonal',\n",
       " 'to_complementary',\n",
       " 'to_directed',\n",
       " 'to_directed_inplace',\n",
       " 'to_dot',\n",
       " 'to_lower_triangular',\n",
       " 'to_main_diagonal',\n",
       " 'to_structural_similarity_multi_graph',\n",
       " 'to_transposed',\n",
       " 'to_upper_triangular',\n",
       " 'validate_edge_id',\n",
       " 'validate_edge_ids',\n",
       " 'validate_edge_type_id',\n",
       " 'validate_edge_type_ids',\n",
       " 'validate_node_id',\n",
       " 'validate_node_ids',\n",
       " 'validate_node_type_id',\n",
       " 'validate_node_type_ids']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3LWYEJqw2Few"
   },
   "source": [
    "Finally, to see a method documentation you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-5Lb34XF2J2l",
    "outputId": "3ef88bcb-4a97-4fde-c44f-b92cd886fb9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function get_node_degrees:\n",
      "\n",
      "get_node_degrees() method of ensmallen.Graph instance\n",
      "    Returns the unweighted degree of every node in the graph\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(graph.get_node_degrees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTLBQJ48x2Lq"
   },
   "source": [
    "### Enabling time-memory tradeoffs\n",
    "The Ensmallen library includes a few time-memory tradeoffs that allow you to choose to spend more memory and get faster execution times. While we do not currently enable these by default because we do not know what you will be doing with the graph object, in most use cases (graphs within a few million nodes) they can be enabled without cornerns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "7unFvwvozGNe"
   },
   "outputs": [],
   "source": [
    "graph.enable()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RYfznH3vxzaV"
   },
   "source": [
    "### Diameter of the graph\n",
    "Computing the diameter of the graph using [ifub](https://who.rocq.inria.fr/Laurent.Viennot/road/papers/ifub.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a1oF3Ntexwkk",
    "outputId": "6fdf0172-41d3-4331-c847-f1cceccf7b43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9 µs, sys: 2 µs, total: 11 µs\n",
      "Wall time: 12.6 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "graph.get_diameter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6O5qo34D0IYH"
   },
   "source": [
    "### Connected components\n",
    "Compute the connected components of the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5fJBF7ph0HgE",
    "outputId": "861f8522-a560-4f18-8c42-598f4482e064"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 5.58 ms, total: 5.58 ms\n",
      "Wall time: 2.27 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "(component_ids, components_number, minimum_component_size, maximum_component_size) = graph.get_connected_components()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LpEJTL590XtY"
   },
   "source": [
    "### Betweenness centrality\n",
    "Compute efficiently the Betweenness centrality using the algorithm described in [Parallel Algorithms for Evaluating Centrality Indices in Real-world Networks](https://ieeexplore.ieee.org/document/1690659/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Sra52g5G0XOk",
    "outputId": "8fe5bbbc-f0c0-4d34-cee4-efaf6a2053dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 3.72 ms, total: 3.72 ms\n",
      "Wall time: 1.94 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1.0500001e+01, 1.1913333e+03, 3.3333334e-01, 5.3333335e+00,\n",
       "       3.3333334e-01, 3.3333334e-01, 3.3333334e-01, 8.3333337e-01,\n",
       "       3.3333334e-01, 3.3333334e-01, 3.3333334e-01, 8.3333337e-01,\n",
       "       8.3333337e-01, 3.3333334e-01, 8.3333337e-01, 3.3333334e-01,\n",
       "       3.3333334e-01, 8.3333337e-01, 8.3333337e-01, 3.3333334e-01,\n",
       "       3.3333334e-01, 8.3333337e-01, 3.3333334e-01, 3.3333334e-01,\n",
       "       3.3333334e-01, 3.3333334e-01, 8.3333337e-01, 2.1666667e+00,\n",
       "       8.3333337e-01, 8.3333337e-01, 1.0000000e+00, 3.3333334e-01,\n",
       "       8.3333337e-01, 8.3333337e-01, 1.0000000e+00, 8.3333337e-01,\n",
       "       3.3333334e-01, 1.3833333e+01, 8.3333337e-01, 3.3333334e-01,\n",
       "       8.3333337e-01, 3.3333334e-01, 3.3333334e-01, 1.0500000e+01,\n",
       "       0.0000000e+00, 3.3333334e-01, 7.1666670e+00, 0.0000000e+00,\n",
       "       3.3333334e-01, 0.0000000e+00, 3.3333334e-01, 3.3333334e-01,\n",
       "       2.2000002e+01, 3.3333334e-01, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "       0.0000000e+00], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "graph.get_betweenness_centrality()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yp5GQY44zwM2"
   },
   "source": [
    "### Visualizing the graph\n",
    "To visualize the graph using dot we can get help from the Embiggen submodule (which is already installed if you have installed from GRAPE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Bk7sKeLX2rwG",
    "outputId": "7f3dccbd-664b-4f6d-8304-153822637c81",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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      "text/plain": [
       "<graphviz.sources.Source at 0x7f3e15c3a640>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from grape.visualizations import GraphVisualizer\n",
    "\n",
    "visualizer = GraphVisualizer(graph)\n",
    "visualizer.plot_dot(engine=\"dot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aAhSetPn28ss"
   },
   "source": [
    "## Compute the graph node embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KiJbMehk3T3k"
   },
   "source": [
    "Let's use TransE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "90G7oHw83Y3p",
    "outputId": "bf937938-58d5-4aee-d8b4-d4275b6b6312"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luca/anaconda3/lib/python3.9/site-packages/embiggen/utils/abstract_models/abstract_embedding_model.py:166: UserWarning: Please be advised that the ZFS graph contains 15 disconnected nodes. Consider that node embedding algorithms that only use topological information such as CBOW, GloVe, SPINE and SkipGram are not able to provide meaningful embeddings for these nodes, and their embedding will be generally far away from any other node. It is also possible that all disconnected nodes will receive a relatively similar node embedding. Consider dropping them by using the `graph.remove_disconnected_nodes()` method.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from grape.embedders import TransEEnsmallen\n",
    "\n",
    "model = TransEEnsmallen()\n",
    "embedding = model.fit_transform(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-pYHh3SC4myC"
   },
   "source": [
    "Let's take a look at the node embedding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 456
    },
    "id": "qE4DADtC4k3l",
    "outputId": "ffa7ebcf-7634-445b-d0ab-b30cc85720a0"
   },
   "outputs": [
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       "OIO:auto-generated-by  0.223521 -0.239980  0.310137  0.519084  0.519116   \n",
       "\n",
       "                             5         6         7         8         9   ...  \\\n",
       "OBO:ZFS_0000047       -0.179307 -0.004260  0.166299 -0.008444  0.077072  ...   \n",
       "OBO:ZFS_0100000       -0.521331  0.088234 -0.122638  0.158635 -0.039027  ...   \n",
       "OBO:ZFS_0000026       -0.023261  0.241839  0.025979 -0.010485  0.168978  ...   \n",
       "OBO:ZFS_0000050        0.063887  0.134699 -0.126789 -0.053283  0.076829  ...   \n",
       "OBO:ZFS_0000040        0.017363 -0.018746 -0.098251  0.055315 -0.254210  ...   \n",
       "...                         ...       ...       ...       ...       ...  ...   \n",
       "rdfs:label             0.491668 -0.092991 -0.569650 -0.080480 -0.302108  ...   \n",
       "OIO:hasDbXref         -0.256800 -0.204276  0.207912 -0.569034 -0.144371  ...   \n",
       "OIO:date               0.527981 -0.397869  0.577103 -0.440254 -0.572563  ...   \n",
       "OIO:created_by        -0.495323 -0.355084 -0.085147 -0.224519  0.032792  ...   \n",
       "OIO:auto-generated-by -0.556312 -0.199711 -0.453098  0.482950 -0.418854  ...   \n",
       "\n",
       "                             90        91        92        93        94  \\\n",
       "OBO:ZFS_0000047       -0.025269 -0.024693  0.038815 -0.061143 -0.091724   \n",
       "OBO:ZFS_0100000       -0.102367  0.120386 -0.005425  0.010858 -0.061239   \n",
       "OBO:ZFS_0000026       -0.055442 -0.114045  0.075020 -0.025065 -0.001077   \n",
       "OBO:ZFS_0000050        0.100896 -0.064437  0.050281  0.067776  0.139759   \n",
       "OBO:ZFS_0000040       -0.028196  0.026919  0.005483  0.002401  0.038716   \n",
       "...                         ...       ...       ...       ...       ...   \n",
       "rdfs:label             0.544844  0.522090 -0.544729  0.191061 -0.174223   \n",
       "OIO:hasDbXref         -0.244415  0.322562 -0.522948  0.460622  0.310926   \n",
       "OIO:date              -0.152701  0.386688  0.549843 -0.495392  0.326002   \n",
       "OIO:created_by        -0.109143 -0.227827  0.541580  0.087527 -0.310132   \n",
       "OIO:auto-generated-by  0.418229  0.149863 -0.169024 -0.279514  0.565224   \n",
       "\n",
       "                             95        96        97        98        99  \n",
       "OBO:ZFS_0000047        0.078599 -0.082917 -0.164953  0.004628  0.018722  \n",
       "OBO:ZFS_0100000       -0.071797  0.089044 -0.032056 -0.051448  0.001933  \n",
       "OBO:ZFS_0000026       -0.037194 -0.270782  0.049454  0.086652  0.080362  \n",
       "OBO:ZFS_0000050        0.051873 -0.069793  0.033216  0.109648  0.203050  \n",
       "OBO:ZFS_0000040       -0.040559  0.081879  0.026882  0.133777 -0.014169  \n",
       "...                         ...       ...       ...       ...       ...  \n",
       "rdfs:label             0.009331  0.280754 -0.020532  0.279475  0.573758  \n",
       "OIO:hasDbXref          0.371484  0.191094 -0.435971  0.126903 -0.454938  \n",
       "OIO:date              -0.057875  0.498005 -0.473571 -0.089911 -0.121154  \n",
       "OIO:created_by        -0.423473  0.455682 -0.469205  0.121412 -0.097890  \n",
       "OIO:auto-generated-by -0.262433 -0.452502  0.214887  0.532065 -0.130561  \n",
       "\n",
       "[77 rows x 100 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding.get_node_embedding_from_index(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OwABr5lm4tz4"
   },
   "source": [
    "And finally the edge types embedding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 222
    },
    "id": "nwkBBEEc4wCp",
    "outputId": "a6d715b8-252f-4556-87c9-54633d3e8dd0"
   },
   "outputs": [
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       "                           0         1         2         3         4   \\\n",
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       "[4 rows x 100 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding.get_edge_type_embedding_from_index(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4YBgVW_q35dX"
   },
   "source": [
    "### Visualizing the computed node embedding\n",
    "We can use the visualizer, transforming the node embedding using a TSNE, to get a visualization of its node degrees and edge types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kPjeuxzR34l5",
    "outputId": "af00a0bd-67b6-49bf-9f96-d9031e9e6443",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n",
      "/home/luca/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1854: UserWarning: y_pred contains classes not in y_true\n",
      "  warnings.warn('y_pred contains classes not in y_true')\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1920x2400 with 27 Axes>"
      ]
     },
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       "<p style=\"text-align: justify; word-break: break-all;\"><b>TSNE decomposition and properties distribution of the ZFS graph using the TransE node embedding:</b> <b>(a)</b> <i>Node degrees heatmap</i>. <b>(b)</b> <i>Detected node ontologies</i>: 'Open Biomedical Ontology' in blue, 'Gene Ontology OBO in OWL' in orange, 'RDFS' in red, 'Basic Formal Ontology' in cyan, 'Relation Ontology' in green, and 'Information Artifact Ontology' in yellow. The node ontologies do not appear to form recognizable clusters (Balanced accuracy: 39.05% ± 10.06%). <b>(c)</b> <i>Connected components</i>: 'Main component' in blue, 'Singletons' in orange, and 'Tuples' in red. The components do not appear to form recognizable clusters (Balanced accuracy: 46.12% ± 15.85%). <b>(d)</b> <i>Existent and non-existent edges</i>: 'Non-existent' in blue and 'Existent' in orange. The existent & non-existent edges form some clusters (Balanced accuracy: 70.32% ± 3.39%). <b>(e)</b> <i>Euclidean distance heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 75.99% ± 1.64%). <b>(f)</b> <i>Cosine similarity heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 75.78% ± 1.26%). Do note that the cosine similarity has been shifted from the range of [-1, 1] to the range [0, 2] to be visualized in a logarithmic heatmap. <b>(g)</b> <i>Adamic-Adar heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 84.07% ± 1.65%). <b>(h)</b> <i>Jaccard Coefficient heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 83.81% ± 3.53%). <b>(j)</b> <i>Preferential Attachment heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 66.86% ± 3.41%). <b>(k)</b> <i>Resource Allocation Index heatmap</i>. This metric is a good edge prediction feature (Balanced accuracy: 84.07% ± 1.65%). <b>(i)</b> <i>Edge types</i>: 'Biolink subclass of' in blue, 'Biolink preceded BY' in orange, 'Biolink part of' in red, and 'owl versionIRI' in cyan. The edge types form some possible clusters (Balanced accuracy: 56.61% ± 7.12%). <b>(l)</b> <i>Euclidean distance distribution.</i> Euclidean distance values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale. <b>(m)</b> <i>Cosine similarity distribution.</i> Cosine similarity values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale. <b>(n)</b> <i>Adamic-Adar distribution.</i> Adamic-Adar values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale. <b>(o)</b> <i>Jaccard Coefficient distribution.</i> Jaccard Coefficient values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale. <b>(p)</b> <i>Preferential Attachment distribution.</i> Preferential Attachment values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale. <b>(q)</b> <i>Resource Allocation Index distribution.</i> Resource Allocation Index values are on the horizontal axis and edge counts are on the vertical axis on a logarithmic scale.<br> In the heatmaps, <b>a</b>, <b>e</b>, <b>f</b>, <b>g</b>, <b>h</b>, <b>j</b>, and <b>k</b>, low and high values appear in red and blue hues, respectively. Intermediate values appear in either a yellow or cyan hue. The values are on a logarithmic scale. The separability considerations for figures <b>b</b>, <b>c</b>, <b>d</b>, <b>e</b>, <b>f</b>, <b>g</b>, <b>h</b>, <b>j</b>, <b>k</b>, and <b>i</b>  derive from evaluating a Decision Tree trained on five Monte Carlo holdouts, with a 70/30 split between training and test sets. We have sampled 149 existing and 149 non-existing edges. We have sampled the non-existent edges' source and destination nodes by avoiding any disconnected nodes present in the graph to avoid biases.</p>"
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       "<IPython.core.display.HTML object>"
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     },
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    }
   ],
   "source": [
    "from grape import GraphVisualizer\n",
    "\n",
    "visualizer = GraphVisualizer(graph)\n",
    "visualizer.fit_and_plot_all(embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA-29EGZrel0"
   },
   "source": [
    "*Would you like this tutorial to include some more sections? Please do let us know [by opening a issue in the GRAPE repository](https://github.com/AnacletoLAB/grape/issues/new).*"
   ]
  }
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