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between_central.py
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between_central.py
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import pandas as pd
import numpy as np
import networkx as nx
import pickle
dataset_edges = pd.read_csv('enrondatasetfinal.csv')
dataset_nodes = pd.read_csv('Enron.true', sep = ';')
node_from = dataset_edges.iloc[:,0].values
node_to = dataset_edges.iloc[:,1].values
num_nodes = len(dataset_nodes)
print num_nodes
# Creare graph
G = nx.Graph()
for i, j in zip(node_from, node_to):
G.add_edge(i, j)
d_bet_cen = dict()
d_bet_cen = nx.betweenness_centrality(G)
output = open('bet_cen_dict.pkl', 'wb')
pickle.dump(d_bet_cen, output)
output.close()
pkl_file = open('bet_cen_dict.pkl','rb')
d_bet_cen =pickle.load(pkl_file)
l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items())
print len(l)
# print min(d_bet_cen.items(), key = lambda x:x[1])
# print d_bet_cen
outfile = open('bet_cen_enron_data.txt', 'w')
for i, j in d_bet_cen.iteritems():
if j >= 0.0000001:
outfile.write(str(i) + " 1\n")
else:
outfile.write(str(i) + " 0\n")
outfile.close()
pkl_file.close()