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python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py
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| @@ -0,0 +1,31 @@ | ||
| # 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 | ||
|
|
||
| from nx_cugraph.algorithms.cluster import _triangles | ||
| from nx_cugraph.convert import _to_graph | ||
| from nx_cugraph.utils import networkx_algorithm | ||
|
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| __all__ = [ | ||
| "is_bipartite", | ||
| ] | ||
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| @networkx_algorithm(plc="triangle_count", version_added="24.02") | ||
| def is_bipartite(G): | ||
| G = _to_graph(G) | ||
| # Counting triangles may not be the fastest way to do this, but it is simple. | ||
| node_ids, triangles, is_single_node = _triangles( | ||
| G, None, symmetrize="union" if G.is_directed() else None | ||
| ) | ||
| return int(cp.count_nonzero(triangles)) == 0 |
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| # 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 pylibcugraph as plc | ||
|
|
||
| from nx_cugraph.convert import _to_undirected_graph | ||
| from nx_cugraph.utils import networkx_algorithm, not_implemented_for | ||
|
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| __all__ = [ | ||
| "triangles", | ||
| "average_clustering", | ||
| "clustering", | ||
| "transitivity", | ||
| ] | ||
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| def _triangles(G, nodes, symmetrize=None): | ||
| if nodes is not None: | ||
| if is_single_node := (nodes in G): | ||
| nodes = [nodes if G.key_to_id is None else G.key_to_id[nodes]] | ||
| else: | ||
| nodes = list(nodes) | ||
| nodes = G._list_to_nodearray(nodes) | ||
| else: | ||
| is_single_node = False | ||
| if len(G) == 0: | ||
| return None, None, is_single_node | ||
| node_ids, triangles = plc.triangle_count( | ||
| resource_handle=plc.ResourceHandle(), | ||
| graph=G._get_plc_graph(symmetrize=symmetrize), | ||
| start_list=nodes, | ||
| do_expensive_check=False, | ||
| ) | ||
| return node_ids, triangles, is_single_node | ||
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| @not_implemented_for("directed") | ||
| @networkx_algorithm(plc="triangle_count", version_added="24.02") | ||
| def triangles(G, nodes=None): | ||
| G = _to_undirected_graph(G) | ||
| node_ids, triangles, is_single_node = _triangles(G, nodes) | ||
| if len(G) == 0: | ||
| return {} | ||
| if is_single_node: | ||
| return int(triangles[0]) | ||
| return G._nodearrays_to_dict(node_ids, triangles) | ||
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| @not_implemented_for("directed") | ||
| @networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") | ||
| def clustering(G, nodes=None, weight=None): | ||
| """Directed graphs and `weight` parameter are not yet supported.""" | ||
| G = _to_undirected_graph(G) | ||
| node_ids, triangles, is_single_node = _triangles(G, nodes) | ||
| if len(G) == 0: | ||
| return {} | ||
| if is_single_node: | ||
| numer = int(triangles[0]) | ||
| if numer == 0: | ||
| return 0 | ||
| degree = int((G.src_indices == nodes).sum()) | ||
| return 2 * numer / (degree * (degree - 1)) | ||
| degrees = G._degrees_array(ignore_selfloops=True)[node_ids] | ||
| denom = degrees * (degrees - 1) | ||
| results = 2 * triangles / denom | ||
| results = cp.where(denom, results, 0) # 0 where we divided by 0 | ||
| return G._nodearrays_to_dict(node_ids, results) | ||
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| @clustering._can_run | ||
| def _(G, nodes=None, weight=None): | ||
| return weight is None and not G.is_directed() | ||
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| @not_implemented_for("directed") | ||
| @networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") | ||
| def average_clustering(G, nodes=None, weight=None, count_zeros=True): | ||
| """Directed graphs and `weight` parameter are not yet supported.""" | ||
| G = _to_undirected_graph(G) | ||
| node_ids, triangles, is_single_node = _triangles(G, nodes) | ||
| if len(G) == 0: | ||
| raise ZeroDivisionError | ||
| degrees = G._degrees_array(ignore_selfloops=True)[node_ids] | ||
| if not count_zeros: | ||
| mask = triangles != 0 | ||
| triangles = triangles[mask] | ||
| if triangles.size == 0: | ||
| raise ZeroDivisionError | ||
| degrees = degrees[mask] | ||
| denom = degrees * (degrees - 1) | ||
| results = 2 * triangles / denom | ||
| if count_zeros: | ||
| results = cp.where(denom, results, 0) # 0 where we divided by 0 | ||
| return float(results.mean()) | ||
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| @average_clustering._can_run | ||
| def _(G, nodes=None, weight=None, count_zeros=True): | ||
| return weight is None and not G.is_directed() | ||
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| @not_implemented_for("directed") | ||
| @networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02") | ||
| def transitivity(G): | ||
| """Directed graphs are not yet supported.""" | ||
| G = _to_undirected_graph(G) | ||
| if len(G) == 0: | ||
| return 0 | ||
| node_ids, triangles = plc.triangle_count( | ||
| resource_handle=plc.ResourceHandle(), | ||
| graph=G._get_plc_graph(), | ||
| start_list=None, | ||
| do_expensive_check=False, | ||
| ) | ||
| numer = int(triangles.sum()) | ||
| if numer == 0: | ||
| return 0 | ||
| degrees = G._degrees_array(ignore_selfloops=True)[node_ids] | ||
| denom = int((degrees * (degrees - 1)).sum()) | ||
| return 2 * numer / denom | ||
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| @transitivity._can_run | ||
| def _(G): | ||
| # Is transitivity supposed to work on directed graphs? | ||
| return not G.is_directed() |
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