独立级联模型.py

import random

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

max_iter_num = 10 # 模拟的次数
G = nx.karate_club_graph() # 空手道俱乐部
print(len(G.nodes))


for edge in G.edges:
    G.add_edge(edge[0], edge[1], weight=round(random.uniform(0,1),2)) # 赋予权值
for node in G:
    G.add_node(node, state = 0) # 用state标识状态 state=0 未激活，state=1 激活

pos = nx.random_layout(G)
weights = nx.get_edge_attributes(G, "weight")
nx.draw(G, with_labels=True)
# nx.draw_networkx_edge_labels(G, pos, edge_labels=weights)
plt.show()

seed = 33 # 选定33作为初始激活节点
G.nodes[seed]['state'] = 1 # 表示33被激活

activated_graph = nx.Graph() # 被激活的图
activated_graph.add_node(seed)

all_active_nodes = [] # 所有被激活的节点放在这里
all_active_nodes.append(seed)

start_influence_nodes = [] # 刚被激活的节点 即有影响力去影响别人的节点
start_influence_nodes.append(seed)

for i in range(max_iter_num):
    new_active = list()
    t1 = '%s time' % i + ' %s nodes' % len(all_active_nodes)
    print(t1) # 当前有多少个节点激活

    # 画图
    plt.title(t1)
    nx.draw(activated_graph, with_labels=True)
    plt.show()

    for v in start_influence_nodes:
        for nbr in G.neighbors(v): 
            if G.nodes[nbr]['state'] == 0: # 如果这个邻居没被激活
                edge_data = G.get_edge_data(v, nbr)
                if random.uniform(0, 1) < edge_data['weight']:
                    G.nodes[nbr]['state'] = 1
                    new_active.append(nbr)
                    activated_graph.add_edge(v, nbr) # 画图 添加边
    
    start_influence_nodes.clear() # 将原先的有个影响力的清空
    start_influence_nodes.extend(new_active) # 将新被激活的节点添加到有影响力
    all_active_nodes.extend(new_active) # 将新被激活的节点添加到激活的列表中
    print('all_active_nodes',all_active_nodes) # 打印