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2-1.py
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2-1.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Feb 2 19:28:15 2018
@author: Administrator
"""
import networkx as nx
import random
import numpy as np
import math
#读取groupmemberships，节点、标签
def readfile(filename):
sim_linelist={}
sim_labeldict={}
labellist = []
b_number = '1'#第一列节点
with open(filename,'r') as f:
for line in f.readlines():
# print(line)
linestr = line.strip()
# print(linestr)
linestrlist = linestr.split("\t")
# print(linestrlist) #输出['','']
linelist = map(int,linestrlist)# linelist = [int(i) for i in linestrlist]
# print(linelist)
sim_linelist[line]=linelist
a=np.array(linestrlist)
gnode_array=a[0]
glabel_array=a[1]
# print(a[0])
# print(a[1])
gnode_list=gnode_array.tolist()
glabel_list=glabel_array.tolist()
if(gnode_list != b_number):
labellist = []#清空列表
labellist.append(glabel_list)#输出{‘’：['']} 节点：标签集
else:
labellist.append(glabel_list)
sim_labeldict[gnode_list] = labellist#节点+标签字典
b_number = str(gnode_list)
# print(gnode_list)
# print(glabel_list)
return sim_labeldict
sim_labeldict = readfile('release-youtube-groupmemberships.txt')
#print(sim_labeldict)
#读入网络links，随机选择节点
G=nx.Graph(nx.read_edgelist('release-youtube-links.txt'))
ledge_list=nx.edges(G)#edges=nx.edges(G) #ledge_list=edges
lnode_list=nx.nodes(G)#nodes=nx.nodes(G) #lnode_list=nodes
#print(ledge_list)
#print(lnode_list)
randomnode = random.choice(lnode_list)#随机节点
#print(randomnode)
randomnode_neighbor = {randomnode}#随机节点邻居
#print(randomnode_neighbor)
while(len(randomnode_neighbor) < 50):
for v in randomnode_neighbor:
randomnode_neighbor = randomnode_neighbor | set(G.neighbors(v))
if len(randomnode_neighbor) > 50:
break
#print(randomnode_neighbor)
#print(len(randomnode_neighbor))
#生成随机节点图
g=nx.Graph()
for u in randomnode_neighbor:
for v in randomnode_neighbor:
if(G.has_edge(u, v)):
g.add_edge(u, v)
g_randomedges=nx.edges(g)
#print(g_randomedges)
#==============================================================================
# node_num=nx.number_of_nodes(g)
# #print(node_num)
# edge_num=nx.number_of_edges(g)
# M = node_num * (node_num - 1) / 2
# s = M / edge_num - 1
# logs = math.log2(s)
#==============================================================================
#将groupmemberships的标签加入到links随机节点图中
key_node=list(sim_labeldict.keys())#sim_labeldict中的节点 gnode_list
#print(key_node)
#slabel_list=list(sim_labeldict.values())#sim_labeldict中的标签 glabel_list
#print(slabel_list)
lnode_dict={}#随机节点加上标签
for v in randomnode_neighbor:
# print (v)
if v in key_node:
# lnode_dict[v]=sim_labeldict.get(v)
lnode_dict[v]=sim_labeldict[v]
# print(lnode_dict)
else:
lnode_dict[v]=[] #sim_labeldict.get(0)#lnode_dict[v]=['']
#print(lnode_dict)
def cluster(g,z):
for z in g:
lnode3=lnode_dict[z]
neighbors=nx.neighbors(g,z)#z的邻居
# print(neighbors)
degree = 0
neighbor_list=[]
for w in neighbors:#z的邻居
lnode4=lnode_dict[w]
if len(set(lnode3) & set(lnode4))>0:#与z有共同标签
degree = degree+1#度
neighbor_list.append(w)
# print(degree)
triangle=0
size = len(neighbor_list)
for u in range(size):
lnode1=lnode_dict[neighbor_list[u]]
for v in range(u+1,size):
lnode2=lnode_dict[neighbor_list[v]]
if (g.has_edge(u,v))&(len(set(lnode1) & set(lnode2))>0):
triangle=triangle+1#三角形
N = (triangle+1)/((degree*(degree-1)/2)-triangle+1)
# print(N)
return N,degree
#N,degree=cluster(g,z)
#print(N)
def GNB(g,method):
node_num=nx.number_of_nodes(g)
edge_num=nx.number_of_edges(g)
M = node_num * (node_num - 1) / 2
s = M / edge_num - 1
logs = math.log2(s)
sim_dict = {} # 存储相似度的字典
# degree_list = []
# if method != 'CN':
# degree_list = degree_of_nodes()#???
# end if
# 计算相似度
ebunch = nx.non_edges(g)
for u, v in ebunch:
s = 0
lnode1=lnode_dict[u]#u的标签
lnode2=lnode_dict[v]#v的标签
if len(set(lnode1) & set(lnode2))>0:
for z in nx.common_neighbors(g, u, v):
N, degree = cluster(g,z)
if method == 'CN':
s += logs + math.log2(N)
elif method == 'AA':
s += 1 / math.log2(degree) * (logs + math.log2(N))
else: # RA
s += 1 / degree * (logs + math.log2(N))
if s > 0:
sim_dict[(u, v)] = s
print(sim_dict)
return sim_dict
#sim_dict=GNB(g,method)
GNB(g,'CN')