/
GFP.py
196 lines (153 loc) · 7.14 KB
/
GFP.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
# Writen by Stephen Bonner 2016
#This file is part of GraphFingerprintComparison.
#GraphFingerprintComparison is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#GraphFingerprintComparison is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#GNU General Public License for more details.
#You should have received a copy of the GNU General Public License
#along with GraphFingerprintComparison. If not, see <http://www.gnu.org/licenses/>.
from graph_tool.all import *
import os, csv
from tabulate import tabulate
import numpy as np
from scipy import stats
import scipy.spatial.distance
from numpy.random import *
import numpy
def implicitExtraction(tempG):
print("Starting Implicit Extraction")
# 1) - number of neigbours of each vertex
tempG.vertex_properties["dp"] = tempG.degree_property_map("total")
# 2) - cluster coeffecient
tempG.vertex_properties["lc"] = local_clustering(tempG)
# 8) Page rank
tempG.vertex_properties["pR"] = pagerank(tempG)
# 9) eigenvector
tempG.vertex_properties["eV"] = eigenvector(tempG)[1]
return tempG
def vertexFeatureExtraction(v, tempG, egoNet):
averageNeighbourhoodScore = 0
twoHopAwayNeighbours = 0
egoNet.a = False
egoNet[v] = True
# iterate over the neighbour vertices of the current vertex
for w in v.out_neighbours():
# 3) - Average number of nodes two hops away
# This is the out degree of the first neighbour (Assuming no parallel edges)
twoHopAwayNeighbours += tempG.vp.dp[w]
# 4) - Average clustering coeffecient of the Neihbourhood of node i
averageNeighbourhoodScore += tempG.vp.lc[w]
# 5) - Ego net
egoNet[w] = True
# Store the computed results in the property maps of the graph
# Fix the possibily of zero degrees
if float(tempG.vp.dp[v]) != 0.0:
tempG.vp.tHN[v] = ((1.0 / float(tempG.vp.dp[v])) * float(twoHopAwayNeighbours))
tempG.vp.nCCP[v] = ((1.0 / float(tempG.vp.dp[v])) * float(averageNeighbourhoodScore))
else:
tempG.vp.tHN[v] = ((1.0 / 1.0) * float(twoHopAwayNeighbours))
tempG.vp.nCCP[v] = ((1.0 / 1.0) * float(averageNeighbourhoodScore))
return tempG
def globalFeatureExtraction(tempG):
# Extract the global features comparing the size of the network
numEdges = tempG.num_edges()
numVertices = tempG.num_vertices()
gc = global_clustering(tempG)
comp, hist = label_components(tempG)
numComponents = len(hist)
d = tempG.degree_property_map("total")
num_triangles = gc[0] * (d.a * (d.a - 1) / 2).sum() / 3
total_hist = vertex_hist(tempG, "total")
degree_max = total_hist[1][len(total_hist[1])-2]
f = [numEdges, numVertices, degree_max, gc[0], numComponents, num_triangles]
return f
def GFPFeatureExtraction(tempG):
print("Starting Feature Extraction")
# Extract the features implcit to graph-tool
tempG = implicitExtraction(tempG)
# Create the property maps to store results-------------------------------------------------------
# 3) - Average number of nodes two hops away.....
tempG.vertex_properties["tHN"] = tempG.new_vertex_property("double")
# 4) - Average clustering coeffecient of the Neihbourhood of node i
tempG.vertex_properties["nCCP"] = tempG.new_vertex_property("double")
# 5) - Number of edges in i's egonet
tempG.vertex_properties["nEEG"] = tempG.new_vertex_property("double")
egoNet = tempG.new_vertex_property("bool")
egoNet.a = False
# 6) - Number of out going edges from the neigbourhood of node i
tempG.vertex_properties["oEEG"] = tempG.new_vertex_property("double")
# 7) - Number of neighbours of the egonet
tempG.vertex_properties["oNEG"] = tempG.new_vertex_property("double")
# A property map for all the visited vertices for the final graph filter
#visitedVertices = tempG.new_vertex_property("bool")
#visitedVertices.a = False
print("Starting Vertex Level Extraction")
for v in tempG.vertices():
tempG = vertexFeatureExtraction(v, tempG, egoNet)
return tempG
def GFPFeatureCreation(tempG):
print("Starting Feature Creation")
# Create vertex * feature matrix
# Loop through all the vertices and extract the vertices and attributes then all to a list
featuresCollection = [ [], [], [], [], [], [] ]
f = []
for v in tempG.vertices():
featuresCollection[0].append(tempG.vp.dp[v])
featuresCollection[1].append(tempG.vp.lc[v])
featuresCollection[2].append(tempG.vp.tHN[v])
featuresCollection[3].append(tempG.vp.nCCP[v])
featuresCollection[4].append(tempG.vp.pR[v])
featuresCollection[5].append(tempG.vp.eV[v])
for i in range(6):
median = numpy.median(featuresCollection[i])
mean = numpy.mean(featuresCollection[i])
stdev = numpy.std(featuresCollection[i])
skewness = stats.skew(featuresCollection[i])
kurtosis = stats.kurtosis(featuresCollection[i])
variance = stats.tvar(featuresCollection[i])
maxVal = stats.tmax(featuresCollection[i])
minVal = stats.tmin(featuresCollection[i])
f += [median, mean, stdev, skewness, kurtosis, variance, maxVal, minVal]
return f
def GFPCompare(f1, f2):
#print("Starting Network Compare")
#print("Bray", abs(scipy.spatial.distance.braycurtis(f1, f2)))
#print("correlation", abs(scipy.spatial.distance.correlation(f1, f2)))
#print("chebyshev" ,abs(scipy.spatial.distance.chebyshev(f1, f2)))
#print("cosine", abs(scipy.spatial.distance.cosine(f1, f2)))
#print("City", abs(scipy.spatial.distance.cityblock(f1, f2)))
return abs(scipy.spatial.distance.canberra(f1, f2))
def GFPSingleFingerprint(tempG):
# Generate a FingerPrint for a single graph
tempG = GFPFeatureExtraction(tempG)
features = GFPFeatureCreation(tempG)
gloalFeatures = globalFeatureExtraction(G1)
return features
#return [features, gloalFeatures]
def GFPControl(G1, G2):
tempG = GFPFeatureExtraction(G1)
features = GFPFeatureCreation(tempG)
tempG2 = GFPFeatureExtraction(G2)
features2 = GFPFeatureCreation(tempG2)
vertexComparison = GFPCompare(features, features2)
gloalFeatures = globalFeatureExtraction(G1)
gloalFeatures2 = globalFeatureExtraction(G2)
globalCompare = GFPCompare(gloalFeatures, gloalFeatures2)
return [vertexComparison, globalCompare]
def sample_k(max):
accept = False
while not accept:
k = np.random.randint(1,max+1)
accept = np.random.random() < 1.0/k
return k
if __name__ == "__main__":
# Testing function with random networks for dev work
print("Testing with random Barabasi networks")
g = price_network(20000, m = 2, directed = False)
#g2 = random_graph(20000, lambda: sample_k(40), model="probabilistic", vertex_corr=lambda i, k: 1.0 / (1 + abs(i - k)), directed=False, n_iter=100)
g2 = price_network(20000, m = 2, directed = False)
print GFPControl(g, g2)