updating the docstrings

.gitignore

@@ -88,6 +88,7 @@ python/nx-cugraph/objects.inv
 
 ## Doxygen and Docs
 cpp/doxygen/html
+cpp/doxygen/xml
 docs/cugraph/lib*
 docs/cugraph/api/*
 

python/cugraph/cugraph/centrality/betweenness_centrality.py

@@ -58,6 +58,12 @@ def betweenness_centrality(
         Algorithm (2001) to compute exact or approximate betweenness.
         If weights are provided in the edgelist, they will not be used.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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 +230,12 @@ def edge_betweenness_centrality(
         The current implementation uses BFS traversals. Use weight parameter
         if weights need to be considered (currently not supported).
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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,

python/cugraph/cugraph/centrality/degree_centrality.py

@@ -28,6 +28,12 @@ def degree_centrality(G, normalized=True):
         cuGraph graph descriptor with connectivity information. The graph can
         contain either directed or undirected edges.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     normalized : bool, optional, default=True
         If True normalize the resulting degree centrality values
 

python/cugraph/cugraph/centrality/eigenvector_centrality.py

@@ -37,6 +37,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/centrality/katz_centrality.py

@@ -48,6 +48,12 @@ def katz_centrality(
         cuGraph graph descriptor with connectivity information. The graph can
         contain either directed or undirected edges.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/community/ecg.py

@@ -38,6 +38,12 @@ def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
         and weights. The adjacency list will be computed if not already
         present.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     min_weight : float, optional (default=0.5)
         The minimum value to assign as an edgeweight in the ECG algorithm.
         It should be a value in the range [0,1] usually left as the default

python/cugraph/cugraph/community/egonet.py

@@ -62,6 +62,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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,
@@ -171,6 +177,12 @@ def batched_ego_graphs(G, seeds, radius=1, center=True, undirected=None, distanc
         information. Edge weights, if present, should be single or double
         precision floating point values.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     seeds : cudf.Series or list or cudf.DataFrame
         Specifies the seeds of the induced egonet subgraphs.
 

python/cugraph/cugraph/community/induced_subgraph.py

@@ -71,6 +71,12 @@ def induced_subgraph(
     G : cugraph.Graph or networkx.Graph
         The current implementation only supports weighted graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/community/ktruss_subgraph.py

@@ -73,6 +73,12 @@ def k_truss(
         defined for only undirected graphs as they are defined for
         undirected triangle in a graph.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     k : int
         The desired k to be used for extracting the k-truss subgraph.
 

python/cugraph/cugraph/community/leiden.py

@@ -49,13 +49,19 @@ def leiden(
 
     Parameters
     ----------
-    G : cugraph.Graph
+    G : cugraph.Graph, networkx.Graph
         cuGraph graph descriptor of type Graph
 
         The current implementation only supports undirected weighted graphs.
 
         The adjacency list will be computed if not already present.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/community/louvain.py

@@ -62,6 +62,12 @@ def louvain(
         present.
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/community/spectral_clustering.py

@@ -45,6 +45,12 @@ def spectralBalancedCutClustering(
     G : cugraph.Graph or networkx.Graph
         Graph descriptor
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     num_clusters : integer
         Specifies the number of clusters to find, must be greater than 1
 
@@ -142,6 +148,12 @@ def spectralModularityMaximizationClustering(
     G : cugraph.Graph or networkx.Graph
         cuGraph graph descriptor. This graph should have edge weights.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     num_clusters : integer
         Specifies the number of clusters to find
 
@@ -233,6 +245,12 @@ def analyzeClustering_modularity(
     G : cugraph.Graph or networkx.Graph
         graph descriptor. This graph should have edge weights.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     n_clusters : integer
         Specifies the number of clusters in the given clustering
 

python/cugraph/cugraph/community/subgraph_extraction.py

@@ -46,6 +46,12 @@ def subgraph(
     G : cugraph.Graph or networkx.Graph
         The current implementation only supports weighted graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/community/triangle_count.py

@@ -54,6 +54,12 @@ def triangle_count(G, start_list=None):
         (edge weights are not used in this algorithm).
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     start_list : list or cudf.Series
         list of vertices for triangle count. if None the entire set of vertices
         in the graph is processed

python/cugraph/cugraph/components/connectivity.py

@@ -115,6 +115,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     directed : bool, optional (default=None)
 
         NOTE
@@ -224,6 +230,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     directed : bool, optional (default=True)
 
         NOTE
@@ -330,6 +342,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     directed : bool, optional (default=True)
 
         NOTE

python/cugraph/cugraph/cores/core_number.py

@@ -37,6 +37,12 @@ def core_number(G, degree_type="bidirectional"):
         of the core numbers.
         The current implementation only supports undirected graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/cores/k_core.py

@@ -55,6 +55,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     k : int, optional (default=None)
         Order of the core. This value must not be negative. If set to None, the
         main core is returned.

python/cugraph/cugraph/link_analysis/hits.py

@@ -34,11 +34,17 @@ def hits(G, max_iter=100, tol=1.0e-5, nstart=None, normalized=True):
 
     Parameters
     ----------
-    G : cugraph.Graph
+    G : cugraph.Graph, networkx.Graph
         cuGraph graph descriptor, should contain the connectivity information
         as an edge list (edge weights are not used for this algorithm).
         The adjacency list will be computed if not already present.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     max_iter : int, optional (default=100)
         The maximum number of iterations before an answer is returned.
 

python/cugraph/cugraph/link_analysis/pagerank.py

@@ -107,6 +107,12 @@ def pagerank(
         as an edge list.
         The transposed adjacency list will be computed if not already present.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     alpha : float, optional (default=0.85)
         The damping factor alpha represents the probability to follow an
         outgoing edge, standard value is 0.85.

python/cugraph/cugraph/link_prediction/jaccard.py

@@ -189,6 +189,12 @@ def jaccard_coefficient(
 
         This implementation only supports undirected, non-multi Graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/link_prediction/overlap.py

@@ -74,6 +74,12 @@ def overlap_coefficient(
 
         This implementation only supports undirected, non-multi edge Graph.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/link_prediction/sorensen.py

@@ -204,6 +204,12 @@ def sorensen_coefficient(
 
         This implementation only supports undirected, non-multi Graphs.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/sampling/node2vec.py

@@ -60,6 +60,12 @@ 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:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     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

python/cugraph/cugraph/sampling/random_walks.py

@@ -72,6 +72,12 @@ def random_walks(
     G : cuGraph.Graph or networkx.Graph
         The graph can be either directed or undirected.
 
+        .. deprecated:: 24.08
+
+           Accepting ``networkx.Graph`` is deprecated and will be removed in a
+           future version.  For ``networkx.Graph`` use netwotkx directly with
+           the ``nx-cugraph`` plug-in.
+
     random_walks_type : str, optional (default='uniform')
         Type of random walks: 'uniform', 'biased', 'node2vec'.
         Only 'uniform' random walks is currently supported