networkx+python实现复杂网络经典的传染病SIR模型(根据度中心性，介数中心性，接近中心性指标选择感染源)

import networkx as nx
import matplotlib.pyplot as plt
import random
import numpy as np

#来生成一个有N个节点，连接概率为p的随机网络
N = 200
p = 0.02
er=nx.erdos_renyi_graph(N,p)

for i in range(N):
    er.nodes[i]['state'] = 'S'
gama = 0.5
beta = 0.1
ps=nx.spring_layout(er)#布置框架
colors={
   "R":'b',"I":'r',"S":'g'}
states= nx.get_node_attributes(er, 'state')############ 获得节点的isCore属性     
color=[colors[states[i]] for i in range(N)]

nx.draw(er,ps,node_color =color ,with_labels=True,node_size=300)
plt.show()

def centrality(G):
    #计算度中心性,降序
    dc = nx.algorithms.centrality.degree_centrality(G)
    return    sorted(dc.items(), key=lambda x: x[