pagerank = nx.pagerank(G, weight='weight', max_iter=100) # Returns dictionary

# print pagerank

f = open(outDirPR + clusterFile, 'w')

for key, value in sorted(pagerank.iteritems(),key=lambda x:(-x[1],x[0])):

# Set k value

k_values = []

# k = 0.0

nodes = G.nodes()

for node in nodes:

k_values.append(G.degree(node))

k_values = sorted(k_values)

k = k_values[len(k_values)/2]

# print clusterFile, k

# print min(k_values)

# print max(k_values)

subG = nx.k_core(G, k=k) # Returns subgraph

# print len(G.nodes()), '\t', len(subG.nodes())

fig1 = plt.figure()

# ax1 = fig1.add_subplot(1,1,1)

# ax1.set_yscale('log')

# ax1.set_xscale('log')

# ax1.set_ylim(bottom=0.8, top = 1e05)

plt.hist(k_values)

plt.savefig('Q3d_2.eps', format='eps', dpi=1000)

# Betweenness

# betw = nx.betweenness_centrality(G, normalized=True, weight='weight')

# print sorted([(k,v) for k,v in betw.iteritems()], key= lambda x:(-x[1],x[0]))

# clsn = nx.closeness_centrality(G, normalized=True)

# print sorted([(k,v) for k,v in clsn.iteritems()], key= lambda x:(-x[1],x[0]))

# evec = nx.eigenvector_centrality(G, weight='weight')

# print sorted([(k,v) for k,v in evec.iteritems()], key= lambda x:(-x[1],x[0]))

katz = nx.katz_centrality(G, normalized=True, weight='weight', alpha=0.005)

matrix = dict()

for node in GSnap.Nodes():

hop = 0

flag = True

while flag == True:

hop += 1

flag = False

NodeVec = snap.TIntV()

# print type(node.GetId())

# print '---------------'

snap.GetNodesAtHop(GSnap, node.GetId(), hop, NodeVec, False)

# print 'g'

for item in NodeVec:

flag = True

if not node.GetId() in matrix:

matrix[node.GetId()] = dict()

matrix[node.GetId()][item] = hop

# print 'i'