GNN学习（一）：基础知识
1 # !usr/bin/env python 2 # -*- coding:utf-8 _*- 3 # @Time :2022/8/20 10:46 4 # @Author: VVZ 5 # @File :1.2.py 6 7 8 import numpy as np 9 import pandas as pd 10 import networkx as nx 11 12 edges = pd.DataFrame() 13 edges['sources'] = [1,1,1,2,2,3,3,4,4,5,5,5] # 起始节点 14 edges['targets'] = [2,4,5,3,1,2,5,1,5,1,3,4] # 终止节点 15 edges['weights'] = [1,1,1,1,1,1,1,1,1,1,1,1] 16 17 G = nx.from_pandas_edgelist(edges, source='sources', target='targets', edge_attr='weights') 18 # degree 19 print('degree:', nx.degree(G)) 20 # 连通分量 21 print('连通分量:', list(nx.connected_components(G))) 22 # 图直径 23 print('图直径:', nx.diameter(G)) 24 # 度中心性 25 print('度中心性:', nx.degree_centrality(G)) 26 # 特征向量中心性 27 print('特征向量中心性:',nx.eigenvector_centrality(G)) 28 # betweenness 29 print('betweenness:', nx.betweenness_centrality(G)) 30 # clossness 31 print('clossness:', nx.closeness_centrality(G)) 32 # pagerank 33 print('pagerank:', nx.pagerank(G)) 34 # HITS 35 print('HITS:', nx.hits(G))
来自b站视频学习
