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MI4.0.py
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MI4.0.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 04 20:27:37 2017
@author: xsjxi
"""
#coding:utf-8
import networkx as nx
import math
#from scipy.special import comb#��Ϸ���
import matplotlib.pyplot as plt
#import warnings
#MI����
def MI(graph_file):
G = nx.read_edgelist(graph_file)
node_num = nx.number_of_nodes(G)
edge_num = nx.number_of_edges(G)
print (node_num)
print (edge_num)
sim_dict = {} # �洢���ƶȵ��ֵ�
I_pConnect_dict = {}
pDisConnect = 1
edges = nx.edges(G)
ebunch = nx.non_edges(G)
nodes = nx.nodes(G)
nodes_Degree_dict = {}
for v in nodes:
nodes_Degree_dict[v] = nx.degree(G, v)
# 需要经常获取顶点的度，因此，可以事先存储下来
# degree_list = [nx.degree(G, v) for v in range(G.number_of_nodes())]
# 下面的两个循环计算$P(L^1_{xy})$，其实我们只需要计算不同度的值$P(L^1_{kxky})$
# =============================================================================
# degree_I_pConnect = {}
# for u, v in edges:
# =============================================================================
for u, v in edges:
uDegree = nodes_Degree_dict[u]
vDegree = nodes_Degree_dict[v]
for i in range(1,vDegree + 1):
pDisConnect = pDisConnect * (((edge_num - uDegree) - i + 1) / (edge_num - i + 1))
pConnect = 1 - pDisConnect
if pConnect == 0:
I_pConnect = -math.log2(0.0001)
else:
I_pConnect = -math.log2(pConnect)
I_pConnect_dict[(u, v)] = I_pConnect
I_pConnect_dict[(v, u)] = I_pConnect
pDisConnect = 1
for m, n in ebunch:
# =============================================================================
# mDegree = nx.degree(G, m)
# nDegree = nx.degree(G, n)
# =============================================================================
mDegree = nodes_Degree_dict[m]
nDegree = nodes_Degree_dict[n]
for i in range(1,nDegree + 1):
pDisConnect = pDisConnect * (((edge_num - mDegree) - i + 1) / (edge_num - i + 1))
pConnect = 1 - pDisConnect
if pConnect == 0:
I_pConnect = -math.log2(0.0001)
else:
I_pConnect = -math.log2(pConnect)
I_pConnect_dict[(m, n)] = I_pConnect
I_pConnect_dict[(n, m)] = I_pConnect
pDisConnect = 1
ebunchs = nx.non_edges(G)
i = 0
# $I(L^1_{xy};z) = I(L^1;z)$，与x, y没有关系，可以先计算出来
for u, v in ebunchs:
pMutual_Information = 0
I_pConnect = I_pConnect_dict[(u, v)]
for z in nx.common_neighbors(G, u, v):
neighbor_num = len(list(nx.neighbors(G,z)))
neighbor_list = nx.neighbors(G,z)
for m in range(len(neighbor_list)):
for n in range(m+1,len(neighbor_list)):
if m!=n:
I_ppConnect = I_pConnect_dict[(neighbor_list[m], neighbor_list[n])]
if nx.clustering(G,z) == 0:
pMutual_Information = pMutual_Information + (2 / (neighbor_num * (neighbor_num - 1))) * ((I_ppConnect) - (-math.log2(0.0001)))
else:
pMutual_Information = pMutual_Information + ( 2/ (neighbor_num * (neighbor_num - 1))) * ((I_ppConnect)-(-math.log2(nx.clustering(G,z))))
sim_dict[(u, v)] = -(I_pConnect - pMutual_Information)
i = i + 1
#print(i)
print(str(u) + "," + str(v))
print (sim_dict[(u, v)])
return sim_dict
#MI('J:\\Python\\LinkPrediction\\Networks\\PB\\polblogs.edgelist')
#MI('J:\\Python\\LinkPrediction\\Networks\\CE\\celegansneural.edgelist')
#MI('J:\\Python\\LinkPrediction\\Networks\\jazz\\jazz.edgelist')
#MI('J:\\Python\\LinkPrediction\\Networks\\Hamster\\Hamster.edgelist')
MI('J:\\Python\\LinkPrediction\\Networks\\test\\test.edgelist')