Source code for

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Alibaba Group Holding Limited. All Rights Reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from import AppAssets
from import not_compatible_for
from import project_to_simple

__all__ = ["katz_centrality"]

[docs]@project_to_simple @not_compatible_for("arrow_property", "dynamic_property") def katz_centrality( graph, alpha=0.1, beta=1.0, tolerance=1e-06, max_round=100, normalized=True, degree_threshold=1e9, ): """Compute the Katz centrality. See more details for Katz centrality here: Args: graph (:class:`graphscope.Graph`): A simple graph. alpha (float, optional): Auttenuation factor. Defaults to 0.1. beta (float, optional): Weight attributed to the immediate neighborhood. Defaults to 1.0. tolerance (float, optional): Error tolerance. Defaults to 1e-06. max_round (int, optional): Maximun number of rounds. Defaults to 100. normalized (bool, optional): Whether to normalize result values. Defaults to True. 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 with the computed katz_centrality, 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.katz_centrality(pg) >>> sess.close() """ alpha = float(alpha) beta = float(beta) tolerance = float(tolerance) max_round = int(max_round) normalized = bool(normalized) degree_threshold = int(degree_threshold) return AppAssets(algo="katz_centrality", context="vertex_data")( graph, alpha, beta, tolerance, max_round, normalized, degree_threshold )