#!/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
#
# 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 graphscope.framework.app import AppAssets
from graphscope.framework.app import not_compatible_for
from graphscope.framework.app 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,
):
"""Compute the Katz centrality.
See more details for Katz centrality here:
https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.katz_centrality_numpy.html
Args:
graph (:class:`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.
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 as gs
g = gs.g()
# Load some data, then project to a simple graph (if needed).
pg = g.project(vertices={"vlabel": []}, edges={"elabel": []})
r = gs.katz_centrality(pg)
s.close()
"""
alpha = float(alpha)
beta = float(beta)
tolerance = float(tolerance)
max_round = int(max_round)
normalized = bool(normalized)
return AppAssets(algo="katz_centrality", context="vertex_data")(
graph,
alpha,
beta,
tolerance,
max_round,
normalized,
)