SIR模型

# simulate the information diffusion under SI model
import networkx as nx
import numpy as np
import random
import matplotlib .pyplot as pltmax_iter_num = 25
g = nx.karate_club_graph()# init the graph with random edge weight and set the inactive status
for edge in g.edges:g.add_edge(edge[0], edge[1], weight=0.01)
for node in g:g.add_node(node, state = 0, weight = 0.1)seed = 33
g.nodes[seed]['state'] = 1all_active_nodes = []
all_active_nodes.append(seed)recover_influence_nodes = []for i in range(max_iter_num):new_active = list()tl = '%s time' % i + ' %s nodes' % len(all_active_nodes)print(tl)# infectionfor v in all_active_nodes:for nbr in g.neighbors(v):# print(nbr)if g.nodes[nbr]['state'] == 0:edge_data = g.get_edge_data(v, nbr)if random.uniform(0, 1) < edge_data['weight']:g.nodes[nbr]['state'] = 1new_active.append(nbr)all_active_nodes.extend(new_active)for v in all_active_nodes:# print(v)if random.uniform(0, 1) < g.nodes[v]['weight']:recover_influence_nodes.append(v)all_active_nodes.remove(v)print('all actived nodes: ', all_active_nodes)print('recover influence nodes: ', recover_influence_nodes)

结点亲密度karate

import networkx as nx
import numpy as np
filename = r"F:\PyCharm\PyCharmProjects\machine-learning\sns_datasets\karate\karate_edges.txt"n = 34
# 初值邻接矩阵
adjacencyMatrix = np.zeros((n, n))
# 亲密度矩阵
intimacy = np.zeros((n, n))
# 结点影响力矩阵
KI = np.zeros(n)
with open(filename) as f:for line in f.readlines():temp_list = line.split()adjacencyMatrix[int(temp_list[0]) - 1][int(temp_list[1]) - 1] = 1adjacencyMatrix[int(temp_list[1]) - 1][int(temp_list[0]) - 1] = 1if __name__ == '__main__':print(adjacencyMatrix)for i in range(len(adjacencyMatrix)):for j in range(len(adjacencyMatrix[0])):print(adjacencyMatrix[i][j], end=' ')print()# 定义一个数组，用来保存每个结点的邻居数neighborsNum = np.zeros(n)# 计算每个结点的邻居数for i in range(len(adjacencyMatrix)):size = 0for j in range(len(adjacencyMatrix[0])):if(adjacencyMatrix[i][j] == 1):size = size + 1neighborsNum[i] = sizeprint("打印每个结点的邻居数")for i in range(len(neighborsNum)):print(neighborsNum[i])# 计算每个结点与邻居结点的交集数量，用以计算初始化亲密度矩阵for i in range(len(adjacencyMatrix)):for j in range(len(adjacencyMatrix[0])):# 如果找到了邻居，那么就对比当前结点所在的行与邻居结点所在的行有多少交集commonNodes = 0if(adjacencyMatrix[i][j] == 1):for k in range(len(adjacencyMatrix[0])):if((adjacencyMatrix[i][k] == 1) and (adjacencyMatrix[i][k] == adjacencyMatrix[j][k])):commonNodes = commonNodes + 1# 求得共同邻居数之后，根据公式计算亲密度intimacy[i][j] = 1 + ((commonNodes)/(neighborsNum[i]+neighborsNum[j]-commonNodes-1))print("共同邻居数与亲密度为", i, j, commonNodes, intimacy[i][j],end=' ')print()# 计算结点的影响力for i in range(len(adjacencyMatrix)):# 定义间接影响力变量indirect = 0for j in range(len(adjacencyMatrix[0])):if(adjacencyMatrix[i][j] == 1):indirect = indirect + intimacy[i][j]*neighborsNum[j]KI[i] = neighborsNum[i] + indirectprint(i,"影响力的值为",KI[i])KI.sort()print(KI)

结点亲密度-USAir97

import networkx as nx
import numpy as np
filename = r"F:\PyCharm\PyCharmProjects\machine-learning\sns_datasets\USAir.txt"n = 334
# 初值邻接矩阵
adjacencyMatrix = np.zeros((n, n))
# 亲密度矩阵
intimacy = np.zeros((n, n))
# 结点影响力矩阵
KI = np.zeros(n)
with open(filename) as f:for line in f.readlines():temp_list = line.split()adjacencyMatrix[int(temp_list[0]) - 1][int(temp_list[1]) - 1] = 1adjacencyMatrix[int(temp_list[1]) - 1][int(temp_list[0]) - 1] = 1if __name__ == '__main__':print(adjacencyMatrix)for i in range(len(adjacencyMatrix)):for j in range(len(adjacencyMatrix[0])):print(adjacencyMatrix[i][j], end=' ')print()# 定义一个数组，用来保存每个结点的邻居数neighborsNum = np.zeros(n)# 计算每个结点的邻居数for i in range(len(adjacencyMatrix)):size = 0for j in range(len(adjacencyMatrix[0])):if(adjacencyMatrix[i][j] == 1):size = size + 1neighborsNum[i] = sizeprint("打印每个结点的邻居数")for i in range(len(neighborsNum)):print(neighborsNum[i])# 计算每个结点与邻居结点的交集数量，用以计算初始化亲密度矩阵for i in range(len(adjacencyMatrix)):for j in range(len(adjacencyMatrix[0])):commonNodes = 0;# 如果找到了邻居，那么就对比当前结点所在的行与邻居结点所在的行有多少交集if(adjacencyMatrix[i][j] == 1):for k in range(len(adjacencyMatrix[0])):if((adjacencyMatrix[i][k] == 1) and (adjacencyMatrix[i][k] == adjacencyMatrix[j][k])):commonNodes = commonNodes + 1# 求得共同邻居数之后，根据公式计算亲密度intimacy[i][j] = 1 + ((commonNodes)/(neighborsNum[i]+neighborsNum[j]-commonNodes-1))print("共同邻居数与亲密度为", i, j, commonNodes, intimacy[i][j],end=' ')print()# 计算结点的影响力for i in range(len(adjacencyMatrix)):# 定义间接影响力变量indirect = 0;for j in range(len(adjacencyMatrix[0])):if(adjacencyMatrix[i][j] == 1):indirect = indirect + intimacy[i][j]*neighborsNum[j]KI[i] = neighborsNum[i] + indirectprint(i,"结点的影响力",KI[i])

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结点重要性与SIR模型基础代码

SIR模型

结点亲密度karate

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