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prepare_data.py
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prepare_data.py
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import os
import networkx as nx
import numpy as np
from networkx.algorithms.centrality import degree_centrality,closeness_centrality, betweenness_centrality
from itertools import groupby
from scipy.stats import entropy
from sklearn.metrics import mutual_info_score
from collections import defaultdict
from scipy.stats import histogram
from gensim.models import Word2Vec
egoList=[0, 107, 1684, 1912 , 3437, 348, 3980, 414, 686, 698]
pathhack = os.path.dirname(os.path.realpath(__file__))
graph_list=[]
for i in egoList:
edge_file = ("./data/ego-facebook/%d.fullEdges" % i)
G=nx.read_edgelist(edge_file)
graph_list.append(G)
nodes= G.nodes()
def DegreeDistribution(G):
degs = defaultdict(int)
for i in G.degree().values(): degs[i]+=1
items = sorted ( degs.items () )
return items
def sim_degree(g1, g2):
degree_list1=[]
degree_list2=[]
degree_dist1= DegreeDistribution(g1)
degree_dist2= DegreeDistribution(g2)
num_node1= len(g1.nodes())
num_node2= len(g2.nodes())
for key, value in degree_dist1:
degree_list1.append(value+1)
#print('degree of node:', key, 'frequency:',value, value+1)
n1= len(degree_list1)
for key, value in degree_dist2:
degree_list2.append(value+1)
n2= len(degree_list2)
if n1>n2:
n= n1-n2
zero_list= [1] * n
degree_list2= degree_list2+zero_list
KL= entropy( degree_list1, degree_list2)
if n1<n2:
n=n2-n1
zero_list= [1] * n
degree_list1= degree_list1+zero_list
KL= entropy( degree_list1, degree_list2)
return KL
def sim_closeness(g1, g2):
closness_list1=[]
closness_list2=[]
closeness_cent1= nx.closeness_centrality(g1)
closeness_cent2= nx.closeness_centrality(g2)
for key, value in closeness_cent1.items():
closness_list1.append(value)
n1= len(g1.nodes())
n2= len(g2.nodes())
numbin= 50
hist1= histogram(closness_list1 ,numbins=numbin)
for i in range(len(hist1[0])):
hist1[0][i]+=1
print(hist1[0])
for key, value in closeness_cent2.items():
closness_list2.append(value)
hist2= histogram(closness_list2,numbins=numbin)
for i in range(len(hist2[0])):
hist2[0][i]+=1
print(hist2[0])
KL= entropy(hist1[0] , hist2[0])
return KL
def sim_betweeness(g1, g2):
betweeness_list1=[]
betweeness_list2=[]
betweeness_cent1= nx.betweenness_centrality(g1)
betweeness_cent2= nx.betweenness_centrality(g2)
n1= len(g1.nodes())
n2=len(g2.nodes())
numbin= 50
for key, value in betweeness_cent1.items():
betweeness_list1.append(value)
hist1= histogram(betweeness_list1 ,numbins=numbin)
for i in range(len(hist1[0])):
hist1[0][i]+=1
for key, value in betweeness_cent2.items():
betweeness_list2.append(value)
hist2= histogram(betweeness_list2,numbins=numbin)
for i in range(len(hist2[0])):
hist2[0][i]+=1
KL= entropy(hist1[0] , hist2[0])
return KL
def sim_eigen(g1, g2):
eigen_list1=[]
eigen_list2=[]
eigen_cent1 = nx.eigenvector_centrality(g1)
eigen_cent2 = nx.eigenvector_centrality(g2)
n1= len(g1.nodes())
n2=len(g2.nodes())
numbin= 50
for key, value in eigen_cent1.items():
eigen_list1.append(value)
hist1= histogram(eigen_list1 ,numbins=numbin)
for i in range(len(hist1[0])):
hist1[0][i]+=1
for key, value in eigen_cent2.items():
eigen_list2.append(value)
hist2= histogram(eigen_list2,numbins=numbin)
for i in range(len(hist2[0])):
hist2[0][i]+=1
KL= entropy(hist1[0] , hist2[0])
return KL
sim_vector=[]
X_train=[]
for i in range(len(graph_list)):
for j in range(len(graph_list)):
if i!=j:
sim_vector=[]
print('ego:',egoList[i],' ego:',egoList[j])
degree1= sim_degree(graph_list[i],graph_list[j])
sim_vector.append(degree1)
print('degree_similaity:',degree1)
closeness1= sim_closeness (graph_list[i],graph_list[j])
sim_vector.append(closeness1)
print('closeness_similaity:',closeness1)
betweeness1= sim_betweeness(graph_list[i],graph_list[j])
sim_vector.append(betweeness1)
print('betweenness_similaity:',betweeness1)
eigen1= sim_eigen(graph_list[i],graph_list[j])
sim_vector.append(eigen1)
print('eigenvector_similaity:',eigen1)
print(sim_vector)
print('---------------------------------------------------------------------')
X_train.append(sim_vector)
X1_train= np.array(X_train)
print(X1_train.shape)
import csv
with open('train_data.csv', 'w', newline='') as fp:
a = csv.writer(fp, delimiter=',')
a.writerows(X1_train)
######################################## Load Embeddings
glo_file = 'glo128.emb'
model = Word2Vec.load_word2vec_format(glo_file, binary=False)
n2vec_file = 'node2vec256_pq2.emb'
model = Word2Vec.load_word2vec_format(n2vec_file, binary=False)
gf_file = open('gf.emb', "r")
gf={}
for line in gf_file:
a= line.rstrip('\n').split(' ')
a1=a[0]
a2= a[1:]
vec= [float(i) for i in a2]
gf.update({a1:vec})
print(gf)
spectral_file = open('ss.emb', "r")
spectral={}
for line in spectral_file:
a= line.rstrip('\n').split(' ')
a1=a[0]
a2= a[1:]
vec= [float(i) for i in a2]
spectral.update({a1:vec})
f3 = open('ego.pairs' ,'w')
f4 = open('ego.sim' ,'w')
for i in range(len(egoList)):
distances=[]
r=1
for j in range(len(egoList)):
if i!= j:
print(egoList[i],egoList[j])
f3.write(str(egoList[i])+' '+str(egoList[j]))
f3.write("\n")
d=np.dot(model[str(egoList[i])], model[str(egoList[j])] )
distances.append((egoList[i],egoList[j], d))
sorted_dist = sorted(distances, key=lambda tup: tup[2], reverse= True)
print('####################################################################')
for k in range(len(sorted_dist)):
f4.write(str(sorted_dist[k][0])+' '+str(sorted_dist[k][1])+' '+str(sorted_dist[k][2]))
print(sorted_dist[k][0], sorted_dist[k][1], sorted_dist[k][2], r)
r+=1
f4.write("\n")
f4.close()
f3.close()