#!/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.
#
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", "lcc"]
[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)
return AppAssets(algo="clustering", context="vertex_data")(graph, degree_threshold)
@project_to_simple
@not_compatible_for("arrow_property", "dynamic_property")
def lcc(graph):
"""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.
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.lcc(pg)
>>> sess.close()
"""
algo = "lcc_directed" if graph.is_directed() else "lcc"
return AppAssets(algo=algo, context="vertex_data")(graph)
[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)