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85 changes: 85 additions & 0 deletions
85
python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| # 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 cupy as cp | ||
| import networkx as nx | ||
| import pylibcugraph as plc | ||
|
|
||
| from nx_cugraph.convert import _to_directed_graph | ||
| from nx_cugraph.utils import ( | ||
| _groupby, | ||
| index_dtype, | ||
| networkx_algorithm, | ||
| not_implemented_for, | ||
| ) | ||
|
|
||
| __all__ = [ | ||
| "number_strongly_connected_components", | ||
| "strongly_connected_components", | ||
| "is_strongly_connected", | ||
| ] | ||
|
|
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|
|
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| def _strongly_connected_components(G): | ||
| # TODO: create utility function to convert just the indices to CSR | ||
| # TODO: this uses a legacy PLC function (strongly_connected_components) | ||
| N = len(G) | ||
| indices = cp.lexsort(cp.vstack((G.dst_indices, G.src_indices))) | ||
| dst_indices = G.dst_indices[indices] | ||
| offsets = cp.searchsorted( | ||
| G.src_indices, cp.arange(N + 1, dtype=index_dtype), sorter=indices | ||
| ).astype(index_dtype) | ||
| labels = cp.zeros(N, dtype=index_dtype) | ||
| plc.strongly_connected_components( | ||
| offsets=offsets, | ||
| indices=dst_indices, | ||
| weights=None, | ||
| num_verts=N, | ||
| num_edges=dst_indices.size, | ||
| labels=labels, | ||
| ) | ||
| return labels | ||
|
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|
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(version_added="24.02", plc="strongly_connected_components") | ||
| def strongly_connected_components(G): | ||
| G = _to_directed_graph(G) | ||
| if G.src_indices.size == 0: | ||
| return [{key} for key in G._nodeiter_to_iter(range(len(G)))] | ||
| labels = _strongly_connected_components(G) | ||
| groups = _groupby(labels, cp.arange(len(G), dtype=index_dtype)) | ||
| return (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) | ||
|
|
||
|
|
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(version_added="24.02", plc="strongly_connected_components") | ||
| def number_strongly_connected_components(G): | ||
| G = _to_directed_graph(G) | ||
| if G.src_indices.size == 0: | ||
| return len(G) | ||
| labels = _strongly_connected_components(G) | ||
| return cp.unique(labels).size | ||
|
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|
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(version_added="24.02", plc="strongly_connected_components") | ||
| def is_strongly_connected(G): | ||
| G = _to_directed_graph(G) | ||
| if len(G) == 0: | ||
| raise nx.NetworkXPointlessConcept( | ||
| "Connectivity is undefined for the null graph." | ||
| ) | ||
| if G.src_indices.size == 0: | ||
| return len(G) == 1 | ||
| labels = _strongly_connected_components(G) | ||
| return bool((labels == labels[0]).all()) |
47 changes: 47 additions & 0 deletions
47
python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| # 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. | ||
| from nx_cugraph.convert import _to_directed_graph | ||
| from nx_cugraph.utils import networkx_algorithm, not_implemented_for | ||
|
|
||
| from .connected import ( | ||
| _connected_components, | ||
| _is_connected, | ||
| _number_connected_components, | ||
| ) | ||
|
|
||
| __all__ = [ | ||
| "number_weakly_connected_components", | ||
| "weakly_connected_components", | ||
| "is_weakly_connected", | ||
| ] | ||
|
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|
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(plc="weakly_connected_components", version_added="24.02") | ||
| def weakly_connected_components(G): | ||
| G = _to_directed_graph(G) | ||
| return _connected_components(G, symmetrize="union") | ||
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(plc="weakly_connected_components", version_added="24.02") | ||
| def number_weakly_connected_components(G): | ||
| G = _to_directed_graph(G) | ||
| return _number_connected_components(G, symmetrize="union") | ||
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| @not_implemented_for("undirected") | ||
| @networkx_algorithm(plc="weakly_connected_components", version_added="24.02") | ||
| def is_weakly_connected(G): | ||
| G = _to_directed_graph(G) | ||
| return _is_connected(G, symmetrize="union") |
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