Use MNMG version of ECG in python layer instead, and remove legacy ECG and Louvain

cpp/tests/lookup/lookup_src_dst_test.cpp

@@ -301,6 +301,6 @@ INSTANTIATE_TEST_SUITE_P(
   Tests_SGLookupEdgeSrcDst_Rmat,
   ::testing::Combine(
     ::testing::Values(EdgeSrcDstLookup_UseCase{false}),
-    ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false))));
+    ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false))));
 
 CUGRAPH_TEST_PROGRAM_MAIN()

cpp/tests/lookup/mg_lookup_src_dst_test.cpp

@@ -344,6 +344,6 @@ INSTANTIATE_TEST_SUITE_P(
   Tests_MGLookupEdgeSrcDst_Rmat,
   ::testing::Combine(
     ::testing::Values(EdgeSrcDstLookup_UseCase{false}),
-    ::testing::Values(cugraph::test::Rmat_Usecase(5, 32, 0.57, 0.19, 0.19, 0, true, false))));
+    ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, true, false))));
 
 CUGRAPH_MG_TEST_PROGRAM_MAIN()

notebooks/algorithms/community/Community-Clustering.ipynb

@@ -78,7 +78,8 @@
     "def compute_clusters(_graph) :\n",
     "\n",
     "    # Compute ECG Clusters and normalize the column names\n",
-    "    _e, _ = cugraph.ecg(_graph).rename(columns={'partition': 'cluster'})\n",
+    "    _e, _ = cugraph.ecg(_graph)\n",
+    "    _e = _e.rename(columns={'partition': 'cluster'})\n",
     "    \n",
     "    # Compute Louvain Clusters \n",
     "    _l, modularity = cugraph.louvain(_graph)\n",
@@ -354,7 +355,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2022, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2022-2024, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",
@@ -379,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.11.9"
   },
   "orig_nbformat": 4,
   "vscode": {

notebooks/algorithms/community/ECG.ipynb

@@ -162,16 +162,7 @@
    "outputs": [],
    "source": [
     "# Call ecg on the graph\n",
-    "df = cugraph.ecg(G) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.dtypes"
+    "df, _ = cugraph.ecg(G)"
    ]
   },
   {
@@ -215,7 +206,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2019-2023, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2019-2024, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",
@@ -240,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.9"
   },
   "vscode": {
    "interpreter": {

python/cugraph/cugraph/community/ecg.py

@@ -14,25 +14,13 @@
 from pylibcugraph import ecg as pylibcugraph_ecg
 from pylibcugraph import ResourceHandle
 
-from cugraph.structure import Graph
 import cudf
-from typing import Union
-from cugraph.utilities import (
-    ensure_cugraph_obj_for_nx,
-    df_score_to_dictionary,
-)
-from cugraph.utilities.utils import import_optional
-
-# FIXME: the networkx.Graph type used in the type annotation for
-# leiden() is specified using a string literal to avoid depending on
-# and importing networkx. Instead, networkx is imported optionally, which may
-# cause a problem for a type checker if run in an environment where networkx is
-# not installed.
-networkx = import_optional("networkx")
+import warnings
+from cugraph.utilities import ensure_cugraph_obj_for_nx, df_score_to_dictionary
 
 
 def ecg(
-    input_graph: Union[Graph, "networkx.Graph"],
+    input_graph,
     min_weight: float = 0.0001,
     ensemble_size: int = 100,
     max_level: int = 10,
@@ -91,6 +79,7 @@ def ecg(
         defaults to a hash of process id, time, and hostname.
 
     weight : str, optional (default=None)
+        Deprecated.
         This parameter is here for NetworkX compatibility and
         represents which NetworkX data column represents Edge weights.
 
@@ -100,9 +89,9 @@ def ecg(
         GPU data frame of size V containing two columns, the vertex id and
         the partition id it is assigned to.
 
-        df[vertex] : cudf.Series
+        parts[vertex] : cudf.Series
             Contains the vertex identifiers
-        df[partition] : cudf.Series
+        parts[partition] : cudf.Series
             Contains the partition assigned to the vertices
 
     modularity_score : float
@@ -119,6 +108,19 @@ def ecg(
 
     input_graph, isNx = ensure_cugraph_obj_for_nx(input_graph)
 
+    if isNx:
+        warning_msg = (
+            " We are deprecating support for handling "
+            "NetworkX types in the next release."
+        )
+        warnings.warn(warning_msg, UserWarning)
+
+    if weight is not None:
+        warning_msg = (
+            "This parameter is deprecated and will be removed in the next release."
+        )
+        warnings.warn(warning_msg, UserWarning)
+
     vertex, partition, modularity_score = pylibcugraph_ecg(
         resource_handle=ResourceHandle(),
         random_state=random_state,
@@ -131,16 +133,14 @@ def ecg(
         do_expensive_check=False,
     )
 
-    df = cudf.DataFrame()
-    df["vertex"] = vertex
-    df["partition"] = partition
+    parts = cudf.DataFrame()
+    parts["vertex"] = vertex
+    parts["partition"] = partition
 
     if input_graph.renumbered:
-        parts = input_graph.unrenumber(df, "vertex")
-    else:
-        parts = df
+        parts = input_graph.unrenumber(parts, "vertex")
 
     if isNx is True:
-        parts = df_score_to_dictionary(df, "partition")
+        parts = df_score_to_dictionary(parts, "partition")
 
     return parts, modularity_score