nx-cugraph: PLC now handles isolated nodes; clean up our workarounds

python/nx-cugraph/_nx_cugraph/__init__.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
@@ -107,21 +107,21 @@
  },
  "extra_docstrings": {
  # BEGIN: extra_docstrings
- "betweenness_centrality": "`weight` parameter is not yet supported.",
+ "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
  "bfs_edges": "`sort_neighbors` parameter is not yet supported.",
  "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.",
  "bfs_successors": "`sort_neighbors` parameter is not yet supported.",
  "bfs_tree": "`sort_neighbors` parameter is not yet supported.",
- "edge_betweenness_centrality": "`weight` parameter is not yet supported.",
+ "edge_betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
  "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.",
- "from_pandas_edgelist": "cudf.DataFrame inputs also supported.",
+ "from_pandas_edgelist": "cudf.DataFrame inputs also supported; value columns with str is unsuppported.",
  "generic_bfs_edges": "`neighbors` and `sort_neighbors` parameters are not yet supported.",
  "k_truss": (
  "Currently raises `NotImplementedError` for graphs with more than one connected\n"
  "component when k >= 3. We expect to fix this soon."
  ),
  "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
- "louvain_communities": "`seed` parameter is currently ignored.",
+ "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
  "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
  # END: extra_docstrings
  },

python/nx-cugraph/nx_cugraph/_version.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.
@@ -11,12 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#
-
 import importlib.resources
 
 # Read VERSION file from the module that is symlinked to VERSION file
-# in the root of the repo at build time or copied to the moudle at
+# in the root of the repo at build time or copied to the module at
 # installation. VERSION is a separate file that allows CI build-time scripts
 # to update version info (including commit hashes) without modifying
 # source files.

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

@@ -23,8 +23,6 @@
  not_implemented_for,
 )
 
-from ..isolate import _isolates
-
 __all__ = ["louvain_communities"]
 
 
@@ -56,7 +54,6 @@ def louvain_communities(
  seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility
  G = _to_undirected_graph(G, weight)
  if G.src_indices.size == 0:
- # TODO: PLC doesn't handle empty graphs gracefully!
  return [{key} for key in G._nodeiter_to_iter(range(len(G)))]
  if max_level is None:
  max_level = 500
@@ -76,14 +73,7 @@ def louvain_communities(
  do_expensive_check=False,
  )
  groups = _groupby(clusters, node_ids, groups_are_canonical=True)
- rv = [set(G._nodearray_to_list(ids)) for ids in groups.values()]
- # TODO: PLC doesn't handle isolated node_ids yet, so this is a temporary fix
- isolates = _isolates(G)
- if isolates.size > 0:
- isolates = isolates[isolates > node_ids.max()]
- if isolates.size > 0:
- rv.extend({node} for node in G._nodearray_to_list(isolates))
- return rv
+ return [set(G._nodearray_to_list(ids)) for ids in groups.values()]
 
 
 @louvain_communities._can_run

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

@@ -10,17 +10,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import itertools
-
 import cupy as cp
 import networkx as nx
 import pylibcugraph as plc
 
 from nx_cugraph.convert import _to_undirected_graph
 from nx_cugraph.utils import _groupby, networkx_algorithm, not_implemented_for
 
-from ..isolate import _isolates
-
 __all__ = [
  "number_connected_components",
  "connected_components",
@@ -32,19 +28,17 @@
 @not_implemented_for("directed")
 @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
- # G = _to_undirected_graph(G)
- # unused_node_ids, labels = plc.weakly_connected_components(
- # resource_handle=plc.ResourceHandle(),
- # graph=G._get_plc_graph(),
- # offsets=None,
- # indices=None,
- # weights=None,
- # labels=None,
- # do_expensive_check=False,
- # )
- # return cp.unique(labels).size
+ G = _to_undirected_graph(G)
+ unused_node_ids, labels = plc.weakly_connected_components(
+ resource_handle=plc.ResourceHandle(),
+ graph=G._get_plc_graph(),
+ offsets=None,
+ indices=None,
+ weights=None,
+ labels=None,
+ do_expensive_check=False,
+ )
+ return cp.unique(labels).size
 
 
 @number_connected_components._can_run
@@ -61,7 +55,6 @@ def _(G):
 def connected_components(G):
  G = _to_undirected_graph(G)
  if G.src_indices.size == 0:
- # TODO: PLC doesn't handle empty graphs (or isolated nodes) gracefully!
  return [{key} for key in G._nodeiter_to_iter(range(len(G)))]
  node_ids, labels = plc.weakly_connected_components(
  resource_handle=plc.ResourceHandle(),
@@ -73,16 +66,7 @@ def connected_components(G):
  do_expensive_check=False,
  )
  groups = _groupby(labels, node_ids)
- it = (G._nodearray_to_set(connected_ids) for connected_ids in groups.values())
- # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix
- isolates = _isolates(G)
- if isolates.size > 0:
- isolates = isolates[isolates > node_ids.max()]
- if isolates.size > 0:
- it = itertools.chain(
- it, ({node} for node in G._nodearray_to_list(isolates))
- )
- return it
+ return (G._nodearray_to_set(connected_ids) for connected_ids in groups.values())
 
 
 @not_implemented_for("directed")
@@ -93,20 +77,16 @@ def is_connected(G):
  raise nx.NetworkXPointlessConcept(
  "Connectivity is undefined for the null graph."
  )
- for community in connected_components(G):
- return len(community) == len(G)
- raise RuntimeError # pragma: no cover
- # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL
- # unused_node_ids, labels = plc.weakly_connected_components(
- # resource_handle=plc.ResourceHandle(),
- # graph=G._get_plc_graph(),
- # offsets=None,
- # indices=None,
- # weights=None,
- # labels=None,
- # do_expensive_check=False,
- # )
- # return labels.size == len(G) and cp.unique(labels).size == 1
+ unused_node_ids, labels = plc.weakly_connected_components(
+ resource_handle=plc.ResourceHandle(),
+ graph=G._get_plc_graph(),
+ offsets=None,
+ indices=None,
+ weights=None,
+ labels=None,
+ do_expensive_check=False,
+ )
+ return bool((labels == labels[0]).all())
 
 
 @not_implemented_for("directed")

python/nx-cugraph/nx_cugraph/tests/__init__.py

@@ -1,5 +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

python/nx-cugraph/nx_cugraph/tests/test_connected.py

@@ -0,0 +1,30 @@
+# Copyright (c) 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
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import networkx as nx
+
+import nx_cugraph as nxcg
+
+
+def test_connected_isolated_nodes():
+ G = nx.complete_graph(4)
+ G.add_node(max(G) + 1)
+ assert nx.is_connected(G) is False
+ assert nxcg.is_connected(G) is False
+ assert nx.number_connected_components(G) == 2
+ assert nxcg.number_connected_components(G) == 2
+ assert sorted(nx.connected_components(G)) == [{0, 1, 2, 3}, {4}]
+ assert sorted(nxcg.connected_components(G)) == [{0, 1, 2, 3}, {4}]
+ assert nx.node_connected_component(G, 0) == {0, 1, 2, 3}
+ assert nxcg.node_connected_component(G, 0) == {0, 1, 2, 3}
+ assert nx.node_connected_component(G, 4) == {4}
+ assert nxcg.node_connected_component(G, 4) == {4}