tynbl.github.io
网络连通性
import networkx as nx
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
G = nx.Graph()
G.add_edges_from([
('A', 'K'),
('A', 'B'),
('A', 'C'),
('B', 'C'),
('B', 'K'),
('C', 'E'),
('C', 'F'),
('D', 'E'),
('E', 'F'),
('E', 'H'),
('F', 'G'),
('I', 'J')
])
plt.figure()
nx.draw_networkx(G)
<IPython.core.display.Javascript object>
1. 整体聚类系数
nx.transitivity(G)
0.4090909090909091
2. 局部聚类系数
print('节点F的聚类系数：', nx.clustering(G, 'F'))
print('节点A的聚类系数：', nx.clustering(G, 'A'))
print('节点J的聚类系数：', nx.clustering(G, 'J'))
节点F的聚类系数： 0.3333333333333333
节点A的聚类系数： 0.6666666666666666
节点J的聚类系数： 0.0
3. 平均聚类系数
nx.average_clustering(G)
0.28787878787878785
4. 节点间距离
# 去掉AC边
G.remove_edge('A', 'C')
plt.figure()
nx.draw_networkx(G)
<IPython.core.display.Javascript object>
nx.shortest_path(G, 'A', 'H')
['A', 'B', 'C', 'E', 'H']
nx.shortest_path_length(G, 'A', 'H')
4