Source code for graphscope.analytical.app.louvain

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2020 Alibaba Group Holding Limited. All Rights Reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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from graphscope.framework.app import AppAssets
from graphscope.framework.app import not_compatible_for
from graphscope.framework.app import project_to_simple
from graphscope.framework.errors import InvalidArgumentError

__all__ = [
    "louvain",
]


[docs]@project_to_simple @not_compatible_for("arrow_property", "dynamic_property") def louvain(graph, min_progress=1000, progress_tries=1): """Compute best partition on the `graph` by louvain. Args: graph (:class:`Graph`): A projected simple undirected graph. min_progress: The minimum delta X required to be considered progress, where X is the number of nodes that have changed their community on a particular pass. Delta X is then the difference in number of nodes that changed communities on the current pass compared to the previous pass. progress_tries: number of times the min_progress setting is not met before exiting form the current level and compressing the graph. Returns: :class:`graphscope.framework.context.VertexDataContextDAGNode`: A context with each vertex assigned with id of community it belongs to, evaluated in eager mode. References: [1] Blondel, V.D. et al. Fast unfolding of communities in large networks. J. Stat. Mech 10008, 1-12(2008). [2] https://github.com/Sotera/distributed-graph-analytics [3] https://sotera.github.io/distributed-graph-analytics/louvain/ Notes: louvain now only support undirected graph. If input graph is directed graph, louvain would raise a InvalidArgumentError. Examples: .. code:: python import graphscope as gs s = gs.session() g = s.g(directed=False) g = g.add_vertices('The parameters for loading a graph...') g = g.add_edges('The parameters for loading a graph...') pg = g.project(vertices={"vlabel": []}, edges={"elabel": ["weight"]}) r = gs.louvain(pg) s.close() """ if graph.is_directed(): raise InvalidArgumentError("Louvain not support directed graph.") return AppAssets(algo="louvain", context="vertex_data")( graph, min_progress, progress_tries )