Merge branch 'branch-24.12' into bug_build

rapidsai · Nov 21, 2024 · de75ea6 · de75ea6
2 parents 9cce314 + c7f2c1d
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diff --git a/docs/cugraph/source/nx_cugraph/benchmarks.md b/docs/cugraph/source/nx_cugraph/benchmarks.md
@@ -9,7 +9,7 @@ We ran several commonly used graph algorithms on both `networkx` and `nx-cugraph
 ![bench-image](../_static/bc_benchmark.png)
 
 <figcaption style="text-align: center;">Results from running this <a
-href="https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/bench_algos.py">Benchmark</a><span
+href="https://github.com/rapidsai/nx-cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/bench_algos.py">Benchmark</a><span
 class="title-ref"></span></figcaption>
 </figure>
 
@@ -23,4 +23,4 @@ Below are the steps to reproduce the results on your own.
 
 4. Install the latest `nx-cugraph` by following the [Installation Guide](installation.md)
 
-5. Follow the instructions written in the README [here](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based)
+5. Follow the instructions written in the README [here](https://github.com/rapidsai/nx-cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based/README.md)
diff --git a/docs/cugraph/source/nx_cugraph/supported-algorithms.rst b/docs/cugraph/source/nx_cugraph/supported-algorithms.rst
@@ -352,4 +352,4 @@ Generators
 
 
 To request nx-cugraph backend support for a NetworkX API that is not listed
-above, visit the `cuGraph GitHub repo <https://github.com/rapidsai/cugraph>`_.
+above, visit the `nx-cugraph GitHub repo <https://github.com/rapidsai/nx-cugraph>`_.
diff --git a/python/cugraph/cugraph/centrality/betweenness_centrality.py b/python/cugraph/cugraph/centrality/betweenness_centrality.py
@@ -58,6 +58,11 @@ def betweenness_centrality(
         Algorithm (2001) to compute exact or approximate betweenness.
         If weights are provided in the edgelist, they will not be used.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     k : int, list or cudf object or None, optional (default=None)
         If k is not None, use k node samples to estimate betweenness. Higher
         values give better approximation.  If k is either a list, a cudf DataFrame,
@@ -224,6 +229,11 @@ def edge_betweenness_centrality(
         The current implementation uses BFS traversals. Use weight parameter
         if weights need to be considered (currently not supported).
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     k : int or list or None, optional (default=None)
         If k is not None, use k node samples to estimate betweenness. Higher
         values give better approximation.  If k is either a list, a cudf DataFrame,

diff --git a/python/cugraph/cugraph/centrality/degree_centrality.py b/python/cugraph/cugraph/centrality/degree_centrality.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 # 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
@@ -28,6 +28,11 @@ def degree_centrality(G, normalized=True):
         cuGraph graph descriptor with connectivity information. The graph can
         contain either directed or undirected edges.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     normalized : bool, optional, default=True
         If True normalize the resulting degree centrality values
 

diff --git a/python/cugraph/cugraph/centrality/eigenvector_centrality.py b/python/cugraph/cugraph/centrality/eigenvector_centrality.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 # 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
@@ -37,6 +37,11 @@ def eigenvector_centrality(G, max_iter=100, tol=1.0e-6):
         cuGraph graph descriptor with connectivity information. The graph can
         contain either directed or undirected edges.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     max_iter : int, optional (default=100)
         The maximum number of iterations before an answer is returned. This can
         be used to limit the execution time and do an early exit before the

diff --git a/python/cugraph/cugraph/centrality/katz_centrality.py b/python/cugraph/cugraph/centrality/katz_centrality.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -48,6 +48,11 @@ def katz_centrality(
         cuGraph graph descriptor with connectivity information. The graph can
         contain either directed or undirected edges.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     alpha : float, optional (default=None)
         Attenuation factor defaulted to None. If alpha is not specified then
         it is internally calculated as 1/(degree_max) where degree_max is the

diff --git a/python/cugraph/cugraph/community/egonet.py b/python/cugraph/cugraph/community/egonet.py
@@ -62,6 +62,11 @@ def ego_graph(G, n, radius=1, center=True, undirected=None, distance=None):
         information. Edge weights, if present, should be single or double
         precision floating point values.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     n : integer or list, cudf.Series, cudf.DataFrame
         A single node as integer or a cudf.DataFrame if nodes are
         represented with multiple columns. If a cudf.DataFrame is provided,

diff --git a/python/cugraph/cugraph/community/induced_subgraph.py b/python/cugraph/cugraph/community/induced_subgraph.py
@@ -71,6 +71,11 @@ def induced_subgraph(
     G : cugraph.Graph or networkx.Graph
         The current implementation only supports weighted graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     vertices : cudf.Series or cudf.DataFrame
         Specifies the vertices of the induced subgraph. For multi-column
         vertices, vertices should be provided as a cudf.DataFrame

diff --git a/python/cugraph/cugraph/community/ktruss_subgraph.py b/python/cugraph/cugraph/community/ktruss_subgraph.py
@@ -50,6 +50,11 @@ def k_truss(
         defined for only undirected graphs as they are defined for
         undirected triangle in a graph.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     k : int
         The desired k to be used for extracting the k-truss subgraph.
 

diff --git a/python/cugraph/cugraph/community/leiden.py b/python/cugraph/cugraph/community/leiden.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -56,6 +56,11 @@ def leiden(
 
         The adjacency list will be computed if not already present.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     max_iter : integer, optional (default=100)
         This controls the maximum number of levels/iterations of the Leiden
         algorithm. When specified the algorithm will terminate after no more

diff --git a/python/cugraph/cugraph/community/louvain.py b/python/cugraph/cugraph/community/louvain.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -62,6 +62,11 @@ def louvain(
         present.
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     max_level : integer, optional (default=100)
         This controls the maximum number of levels of the Louvain
         algorithm. When specified the algorithm will terminate after no more

diff --git a/python/cugraph/cugraph/community/spectral_clustering.py b/python/cugraph/cugraph/community/spectral_clustering.py
@@ -45,6 +45,11 @@ def spectralBalancedCutClustering(
     G : cugraph.Graph or networkx.Graph
         Graph descriptor
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     num_clusters : integer
         Specifies the number of clusters to find, must be greater than 1
 
@@ -142,6 +147,11 @@ def spectralModularityMaximizationClustering(
     G : cugraph.Graph or networkx.Graph
         cuGraph graph descriptor. This graph should have edge weights.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     num_clusters : integer
         Specifies the number of clusters to find
 
@@ -233,6 +243,11 @@ def analyzeClustering_modularity(
     G : cugraph.Graph or networkx.Graph
         graph descriptor. This graph should have edge weights.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     n_clusters : integer
         Specifies the number of clusters in the given clustering
 

diff --git a/python/cugraph/cugraph/community/subgraph_extraction.py b/python/cugraph/cugraph/community/subgraph_extraction.py
@@ -46,6 +46,11 @@ def subgraph(
     G : cugraph.Graph or networkx.Graph
         The current implementation only supports weighted graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     vertices : cudf.Series or cudf.DataFrame
         Specifies the vertices of the induced subgraph. For multi-column
         vertices, vertices should be provided as a cudf.DataFrame

diff --git a/python/cugraph/cugraph/community/triangle_count.py b/python/cugraph/cugraph/community/triangle_count.py
@@ -54,6 +54,11 @@ def triangle_count(G, start_list=None):
         (edge weights are not used in this algorithm).
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     start_list : list or cudf.Series
         list of vertices for triangle count. if None the entire set of vertices
         in the graph is processed

diff --git a/python/cugraph/cugraph/components/connectivity.py b/python/cugraph/cugraph/components/connectivity.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -115,6 +115,11 @@ def weakly_connected_components(G, directed=None, connection=None, return_labels
         The adjacency list will be computed if not already present. The number
         of vertices should fit into a 32b int.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     directed : bool, optional (default=None)
 
         NOTE
@@ -224,6 +229,11 @@ def strongly_connected_components(
         The adjacency list will be computed if not already present.  The number
         of vertices should fit into a 32b int.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     directed : bool, optional (default=True)
 
         NOTE
@@ -330,6 +340,11 @@ def connected_components(G, directed=None, connection="weak", return_labels=None
         The adjacency list will be computed if not already present.  The number
         of vertices should fit into a 32b int.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     directed : bool, optional (default=True)
 
         NOTE

diff --git a/python/cugraph/cugraph/cores/core_number.py b/python/cugraph/cugraph/cores/core_number.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -37,6 +37,11 @@ def core_number(G, degree_type="bidirectional"):
         of the core numbers.
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     degree_type: str, (default="bidirectional")
         This option determines if the core number computation should be based
         on input, output, or both directed edges, with valid values being

diff --git a/python/cugraph/cugraph/cores/k_core.py b/python/cugraph/cugraph/cores/k_core.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # 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
@@ -55,6 +55,11 @@ def k_core(G, k=None, core_number=None, degree_type="bidirectional"):
         weights, they don't participate in the calculation of the k-core.
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     k : int, optional (default=None)
         Order of the core. This value must not be negative. If set to None, the
         main core is returned.

diff --git a/python/cugraph/cugraph/link_analysis/pagerank.py b/python/cugraph/cugraph/link_analysis/pagerank.py
@@ -107,6 +107,11 @@ def pagerank(
         as an edge list.
         The transposed adjacency list will be computed if not already present.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     alpha : float, optional (default=0.85)
         The damping factor alpha represents the probability to follow an
         outgoing edge, standard value is 0.85.

diff --git a/python/cugraph/cugraph/link_prediction/cosine.py b/python/cugraph/cugraph/link_prediction/cosine.py
@@ -190,6 +190,11 @@ def cosine_coefficient(
 
         This implementation only supports undirected, non-multi Graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None)
         A GPU dataframe consisting of two columns representing pairs of
         vertices or iterable of 2-tuples (u, v) where u and v are nodes in

diff --git a/python/cugraph/cugraph/link_prediction/jaccard.py b/python/cugraph/cugraph/link_prediction/jaccard.py
@@ -190,6 +190,11 @@ def jaccard_coefficient(
 
         This implementation only supports undirected, non-multi Graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None)
         A GPU dataframe consisting of two columns representing pairs of
         vertices or iterable of 2-tuples (u, v) where u and v are nodes in

diff --git a/python/cugraph/cugraph/link_prediction/overlap.py b/python/cugraph/cugraph/link_prediction/overlap.py
@@ -75,6 +75,11 @@ def overlap_coefficient(
 
         This implementation only supports undirected, non-multi edge Graph.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None)
         A GPU dataframe consisting of two columns representing pairs of
         vertices or iterable of 2-tuples (u, v) where u and v are nodes in

diff --git a/python/cugraph/cugraph/link_prediction/sorensen.py b/python/cugraph/cugraph/link_prediction/sorensen.py
@@ -205,6 +205,11 @@ def sorensen_coefficient(
 
         This implementation only supports undirected, non-multi Graphs.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None)
         A GPU dataframe consisting of two columns representing pairs of
         vertices or iterable of 2-tuples (u, v) where u and v are nodes in

diff --git a/python/cugraph/cugraph/sampling/node2vec.py b/python/cugraph/cugraph/sampling/node2vec.py
@@ -60,6 +60,11 @@ def node2vec(G, start_vertices, max_depth=1, compress_result=True, p=1.0, q=1.0)
         The graph can be either directed or undirected.
         Weights in the graph are ignored.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     start_vertices: int or list or cudf.Series or cudf.DataFrame
         A single node or a list or a cudf.Series of nodes from which to run
         the random walks. In case of multi-column vertices it should be

diff --git a/python/cugraph/cugraph/sampling/random_walks.py b/python/cugraph/cugraph/sampling/random_walks.py
@@ -72,6 +72,11 @@ def random_walks(
     G : cuGraph.Graph or networkx.Graph
         The graph can be either directed or undirected.
 
+        .. deprecated:: 24.12
+           Accepting a ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use networkx directly with
+           the ``nx-cugraph`` backend. See:  https://rapids.ai/nx-cugraph/
+
     random_walks_type : str, optional (default='uniform')
         Type of random walks: 'uniform', 'biased', 'node2vec'.
         Only 'uniform' random walks is currently supported
Original file line number	Diff line number	Diff line change
Expand Up		@@ -352,4 +352,4 @@ Generators


		To request nx-cugraph backend support for a NetworkX API that is not listed
		above, visit the `cuGraph GitHub repo <https://github.com/rapidsai/cugraph>`_.
		above, visit the `nx-cugraph GitHub repo <https://github.com/rapidsai/nx-cugraph>`_.