Source code for graphscope.analytical.app.clustering

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
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# Copyright 2020 Alibaba Group Holding Limited. All Rights Reserved.
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#     http://www.apache.org/licenses/LICENSE-2.0
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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

__all__ = ["avg_clustering", "clustering"]


[docs]@project_to_simple @not_compatible_for("arrow_property", "dynamic_property") def clustering(graph, degree_threshold=1000000000): """Local clustering coefficient of a node in a Graph is the fraction of pairs of the node’s neighbors that are adjacent to each other. Args: graph (:class:`graphscope.Graph`): A simple graph. degree_threshold (int, optional): Filter super vertex which degree is greater than threshold. Default to 1e9. Returns: :class:`graphscope.framework.context.VertexDataContextDAGNode`: A context with each vertex assigned the computed clustering value, will be evaluated in eager mode. Examples: .. code:: python >>> import graphscope >>> from graphscope.dataset import load_p2p_network >>> sess = graphscope.session(cluster_type="hosts", mode="eager") >>> g = load_p2p_network(sess) >>> # project to a simple graph (if needed) >>> pg = g.project(vertices={"host": ["id"]}, edges={"connect": ["dist"]}) >>> c = graphscope.clustering(pg) >>> sess.close() """ degree_threshold = int(degree_threshold) if graph.is_directed(): return AppAssets(algo="clustering", context="vertex_data")( graph, degree_threshold ) else: return AppAssets(algo="lcc", context="vertex_data")(graph, degree_threshold)
[docs]@project_to_simple @not_compatible_for("arrow_property", "dynamic_property", "undirected") def avg_clustering(graph, degree_threshold=1000000000): """Compute the average clustering coefficient for the directed graph. Args: graph (:class:`graphscope.Graph`): A simple graph. degree_threshold (int, optional): Filter super vertex which degree is greater than threshold. Default to 1e9. Returns: r: float The average clustering coefficient. Examples: .. code:: python >>> import graphscope >>> from graphscope.dataset import load_p2p_network >>> sess = graphscope.session(cluster_type="hosts", mode="eager") >>> g = load_p2p_network(sess) >>> # project to a simple graph >>> pg = g.project(vertices={"host": ["id"]}, edges={"connect": ["dist"]}) >>> c = graphscope.avg_clustering(pg) >>> print(c.to_numpy("r", axis=0)[0]) >>> sess.close() """ degree_threshold = int(degree_threshold) return AppAssets(algo="avg_clustering", context="tensor")(graph, degree_threshold)