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This PR adds an MG implementation of K-Truss leveraging the C API Authors: - Joseph Nke (https://github.com/jnke2016) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) - Jake Awe (https://github.com/AyodeAwe) URL: #4438
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python/cugraph/cugraph/dask/community/ktruss_subgraph.py
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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. | ||
# | ||
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from typing import Tuple | ||
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import cudf | ||
import cupy as cp | ||
from dask.distributed import wait, default_client | ||
import dask_cudf | ||
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from pylibcugraph import ( | ||
ResourceHandle, | ||
k_truss_subgraph as pylibcugraph_k_truss_subgraph, | ||
) | ||
import cugraph.dask.comms.comms as Comms | ||
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def _call_k_truss_subgraph( | ||
sID: bytes, | ||
mg_graph_x, | ||
k: int, | ||
do_expensive_check: bool, | ||
) -> Tuple[cp.ndarray, cp.ndarray, cp.ndarray]: | ||
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return pylibcugraph_k_truss_subgraph( | ||
resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), | ||
graph=mg_graph_x, | ||
k=k, | ||
do_expensive_check=do_expensive_check, | ||
) | ||
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def convert_to_cudf(cp_arrays: cp.ndarray) -> cudf.DataFrame: | ||
cp_src, cp_dst, cp_weight, _ = cp_arrays | ||
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df = cudf.DataFrame() | ||
if cp_src is not None: | ||
df["src"] = cp_src | ||
df["dst"] = cp_dst | ||
if cp_weight is not None: | ||
df["weight"] = cp_weight | ||
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return df | ||
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def ktruss_subgraph(input_graph, k: int) -> dask_cudf.DataFrame: | ||
""" | ||
Returns the K-Truss subgraph of a graph for a specific k. | ||
The k-truss of a graph is a subgraph where each edge is incident to at | ||
least (k−2) triangles. K-trusses are used for finding tighlty knit groups | ||
of vertices in a graph. A k-truss is a relaxation of a k-clique in the graph. | ||
Finding cliques is computationally demanding and finding the maximal | ||
k-clique is known to be NP-Hard. | ||
Parameters | ||
---------- | ||
input_graph : cugraph.Graph | ||
Graph or matrix object, which should contain the connectivity | ||
information. Edge weights, if present, should be single or double | ||
precision floating point values | ||
k : int | ||
The desired k to be used for extracting the k-truss subgraph. | ||
Returns | ||
------- | ||
k_truss_edge_lists : dask_cudf.DataFrame | ||
Distributed GPU data frame containing all source identifiers, | ||
destination identifiers, and edge weights belonging to the truss. | ||
""" | ||
if input_graph.is_directed(): | ||
raise ValueError("input graph must be undirected") | ||
# Initialize dask client | ||
client = default_client() | ||
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do_expensive_check = False | ||
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result = [ | ||
client.submit( | ||
_call_k_truss_subgraph, | ||
Comms.get_session_id(), | ||
input_graph._plc_graph[w], | ||
k, | ||
do_expensive_check, | ||
workers=[w], | ||
allow_other_workers=False, | ||
) | ||
for w in Comms.get_workers() | ||
] | ||
wait(result) | ||
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cudf_result = [client.submit(convert_to_cudf, cp_arrays) for cp_arrays in result] | ||
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wait(cudf_result) | ||
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ddf = dask_cudf.from_delayed(cudf_result).persist() | ||
wait(ddf) | ||
# Wait until the inactive futures are released | ||
wait([(r.release(), c_r.release()) for r, c_r in zip(result, cudf_result)]) | ||
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if input_graph.renumbered: | ||
ddf = input_graph.unrenumber(ddf, "src") | ||
ddf = input_graph.unrenumber(ddf, "dst") | ||
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return ddf |
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