graph_laplacian.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
from sklearn.cross_validation import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import confusion_matrix
from numpy import linalg as LA

#Numpy error
def laplacian(G):
	# Normalized Laplacian Matrix
	# L = 1 - D^(-1/2).W.D^(-1/2)
	# D = degree matrix
	# W = adjacency matrix
	W = nx.adjacency_matrix(G)
	D = []
	for i in range(0, len(G.nodes())):
	    temp = np.zeros(len(G.nodes()))
	    temp[i] = len(list(G.neighbors(list(G.nodes())[i])))
	    D.append(temp)

	D = np.array(D)
	W = np.array(W)
	L = np.array([])
	L = np.matmul(W, np.sqrt(D))
	return L

def eigenvector_matrix(G):
	W = np.array(nx.adjacency_matrix(G).todense())
	print W.shape[0], W.shape[1]
	return LA.eig(W)
	


dataset_edges = pd.read_csv('./Enron/enron_dataset_final.csv')
dataset_nodes = pd.read_csv('./Enron/Enron.true', sep = ';')

node_from = dataset_edges.iloc[:,0].values
node_to = dataset_edges.iloc[:,1].values

num_nodes = len(dataset_nodes)
print "Number of nodes: " +  str(num_nodes)

#Stores the node as key, it's neighbors as values
d = dict()
for i in range(0, (num_nodes+1)):
	d[i] = []
for (i,j) in zip(node_from, node_to):
	if j not in d[i]:
		d[i].append(j)
	if i not in d[j]:
		d[j].append(i)

train_nodes = int(0.8*13533)    #10826 
test_nodes = int(0.2*13533) 	#2706
print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes)

#Full dataset graph
H = nx.Graph()
for k, v in d.iteritems():
	for j in v:
		H.add_edge(k, j)
# nx.write_graphml(H, 'dataset_graph.graphml')
# H = nx.read_graphml('dataset_graph.graphml')

# Training graph
G = nx.Graph()
for i in range(train_nodes):
	for j in d[i]:
		G.add_edge(i, j)
# nx.write_graphml(G, 'train_graph.graphml')
# G = nx.read_graphml('train_graph.graphml')

#Testing graph
test_id = [i for i in H.nodes() if i not in G.nodes()]
P = nx.Graph()
for i in test_id:
	for j in d[i]:
		P.add_edge(i, j)
# nx.write_graphml(P, 'test_graph.graphml')
# P = nx.read_graphml('test_graph.graphml')

print len(H.nodes())
print len(G.nodes())
print len(P.nodes())

L = nx.normalized_laplacian_matrix(G)
# print L
# w: eigenvector, v: eigenvalue
# w,v = eigenvector_matrix(G)