nx-cugraph: indicate which plc algorithms are used and version_added

python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -24,7 +24,7 @@
 ]
 
 
-@networkx_algorithm(nodes_or_number=[0, 1])
+@networkx_algorithm(nodes_or_number=[0, 1], version_added="23.12")
 def complete_bipartite_graph(n1, n2, create_using=None):
     graph_class, inplace = _create_using_class(create_using)
     if graph_class.is_directed():

python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -18,11 +18,16 @@
 __all__ = ["betweenness_centrality", "edge_betweenness_centrality"]
 
 
-@networkx_algorithm
+@networkx_algorithm(
+    is_incomplete=True,  # weight not supported
+    is_different=True,  # RNG with seed is different
+    plc="betweenness_centrality",
+    version_added="23.10",
+)
 def betweenness_centrality(
     G, k=None, normalized=True, weight=None, endpoints=False, seed=None
 ):
-    """`weight` parameter is not yet supported."""
+    """`weight` parameter is not yet supported, and RNG with seed may be different."""
     if weight is not None:
         raise NotImplementedError(
             "Weighted implementation of betweenness centrality not currently supported"
@@ -46,9 +51,14 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None):
     return weight is None
 
 
-@networkx_algorithm
+@networkx_algorithm(
+    is_incomplete=True,  # weight not supported
+    is_different=True,  # RNG with seed is different
+    plc="edge_betweenness_centrality",
+    version_added="23.10",
+)
 def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None):
-    """`weight` parameter is not yet supported."""
+    """`weight` parameter is not yet supported, and RNG with seed may be different."""
     if weight is not None:
         raise NotImplementedError(
             "Weighted implementation of betweenness centrality not currently supported"

python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -16,7 +16,7 @@
 __all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"]
 
 
-@networkx_algorithm
+@networkx_algorithm(version_added="23.12")
 def degree_centrality(G):
     G = _to_graph(G)
     if len(G) <= 1:
@@ -27,7 +27,7 @@ def degree_centrality(G):
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm
+@networkx_algorithm(version_added="23.12")
 def in_degree_centrality(G):
     G = _to_directed_graph(G)
     if len(G) <= 1:
@@ -38,7 +38,7 @@ def in_degree_centrality(G):
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm
+@networkx_algorithm(version_added="23.12")
 def out_degree_centrality(G):
     G = _to_directed_graph(G)
     if len(G) <= 1:

python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -26,7 +26,12 @@
 
 
 @not_implemented_for("multigraph")
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(
+    extra_params=_dtype_param,
+    is_incomplete=True,  # nstart not supported
+    plc="eigenvector_centrality",
+    version_added="23.12",
+)
 def eigenvector_centrality(
     G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None
 ):

python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -26,7 +26,12 @@
 
 
 @not_implemented_for("multigraph")
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(
+    extra_params=_dtype_param,
+    is_incomplete=True,  # nstart and normalized=False not supported
+    plc="katz_centrality",
+    version_added="23.12",
+)
 def katz_centrality(
     G,
     alpha=0.1,

python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -35,7 +35,11 @@
             "Upper limit of the number of macro-iterations (max: 500)."
         ),
         **_dtype_param,
-    }
+    },
+    is_incomplete=True,  # seed not supported; self-loops not supported
+    is_different=True,  # RNG different
+    plc="louvain",
+    version_added="23.10",
 )
 def louvain_communities(
     G,
@@ -47,7 +51,7 @@ def louvain_communities(
     max_level=None,
     dtype=None,
 ):
-    """`seed` parameter is currently ignored."""
+    """`seed` parameter is currently ignored, and self-loops are not yet supported."""
     # NetworkX allows both directed and undirected, but cugraph only allows undirected.
     seed = _seed_to_int(seed)  # Unused, but ensure it's valid for future compatibility
     G = _to_undirected_graph(G, weight)

python/nx-cugraph/nx_cugraph/algorithms/components/connected.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -30,7 +30,7 @@
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
 def number_connected_components(G):
     return sum(1 for _ in connected_components(G))
     # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL
@@ -57,7 +57,7 @@ def _(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
 def connected_components(G):
     G = _to_undirected_graph(G)
     if G.src_indices.size == 0:
@@ -86,7 +86,7 @@ def connected_components(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
 def is_connected(G):
     G = _to_undirected_graph(G)
     if len(G) == 0:
@@ -110,7 +110,7 @@ def is_connected(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
 def node_connected_component(G, n):
     # We could also do plain BFS from n
     G = _to_undirected_graph(G)

python/nx-cugraph/nx_cugraph/algorithms/core.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -27,7 +27,7 @@
 
 @not_implemented_for("directed")
 @not_implemented_for("multigraph")
-@networkx_algorithm
+@networkx_algorithm(is_incomplete=True, plc="k_truss_subgraph", version_added="23.12")
 def k_truss(G, k):
     """
     Currently raises `NotImplementedError` for graphs with more than one connected

python/nx-cugraph/nx_cugraph/algorithms/dag.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -45,11 +45,11 @@ def _ancestors_and_descendants(G, source, *, is_ancestors):
     return G._nodearray_to_set(node_ids[mask])
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs", version_added="24.02")
 def descendants(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=False)
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs", version_added="24.02")
 def ancestors(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=True)

python/nx-cugraph/nx_cugraph/algorithms/isolate.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -25,7 +25,7 @@
 __all__ = ["is_isolate", "isolates", "number_of_isolates"]
 
 
-@networkx_algorithm
+@networkx_algorithm(version_added="23.10")
 def is_isolate(G, n):
     G = _to_graph(G)
     index = n if G.key_to_id is None else G.key_to_id[n]
@@ -51,13 +51,13 @@ def _isolates(G) -> cp.ndarray[IndexValue]:
     return cp.nonzero(_mark_isolates(G))[0]
 
 
-@networkx_algorithm
+@networkx_algorithm(version_added="23.10")
 def isolates(G):
     G = _to_graph(G)
     return G._nodeiter_to_iter(iter(_isolates(G).tolist()))
 
 
-@networkx_algorithm
+@networkx_algorithm(version_added="23.10")
 def number_of_isolates(G):
     G = _to_graph(G)
     return _mark_isolates(G).sum().tolist()

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -32,7 +32,9 @@
             "The edge attribute to use as the edge weight."
         ),
         **_dtype_param,
-    }
+    },
+    plc="hits",
+    version_added="23.12",
 )
 def hits(
     G,

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -26,7 +26,12 @@
 __all__ = ["pagerank"]
 
 
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(
+    extra_params=_dtype_param,
+    is_incomplete=True,  # dangling not supported
+    plc={"pagerank", "personalized_pagerank"},
+    version_added="23.12",
+)
 def pagerank(
     G,
     alpha=0.85,
@@ -97,7 +102,7 @@ def pagerank(
 
 
 @pagerank._can_run
-def pagerank(
+def _(
     G,
     alpha=0.85,
     personalization=None,

python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -21,12 +21,12 @@
 __all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"]
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs", version_added="23.12")
 def single_source_shortest_path_length(G, source, cutoff=None):
     return _single_shortest_path_length(G, source, cutoff, "Source")
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs", version_added="23.12")
 def single_target_shortest_path_length(G, target, cutoff=None):
     return _single_shortest_path_length(G, target, cutoff, "Target")