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createVector.py
186 lines (150 loc) · 4.28 KB
/
createVector.py
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#!/usr/bin/python2
# -*-coding:Utf-8 -*
import networkx as nx
import numpy as np
import cmath
import math
# Defining some useful functions to get the features
def numberLeaves(G):
nl=0
l=nx.nodes(G)
for elt in l:
n=1
ite=nx.all_neighbors(G,elt)
while 1:
try:
ite.next()
n+=1
except StopIteration:
n-=1
break
if n==1:
nl+=1
return nl
def graphEnergy (G):
energy=0
adjSpectre = nx.adjacency_spectrum(G)
for elt in adjSpectre:
if type(elt)!=complex:
energy+=abs(elt)
return energy
def averageNeighDegree(G):
sum=0
l=nx.nodes(G)
for elt in l:
sum+=G.degree(elt)
avg=sum/(nx.number_of_nodes(G))
return avg
def richClub(G):
l1=nx.nodes(G)
l2=[]
for elt in l1:
if G.degree(elt)>=1:
l2.append(elt)
if len(l2)<2:
return 0
else:
adjk=G.subgraph(l2)
phi=2*nx.number_of_edges(adjk)/(len(l2)*(len(l2)-1))
return phi
def metric(G):
k=0
for n1,n2 in nx.edges(G):
k+=G.degree(n1)*G.degree(n2)
return k
def average_closeness(G):
sum=0
for nod in nx.nodes(G):
sum+=nx.closeness_centrality(G,nod)
avg=sum/(nx.number_of_nodes(G))
return avg
def creationVecteur (G):
v={}
# Adding nodes
nn = nx.number_of_nodes(G)
v["numNodes"]=nn
# Adding edges
ne = nx.number_of_edges(G)
v["numEdges"]=ne
# Adding cyclomatic number
c=nx.number_connected_components(G)
cyclo = ne-nn+c
v["numCycles"]=cyclo
# Adding link density
if nn==1:
linkdensity="?"
else:
linkdensity = 2*ne/((nn-1)*nn)
v["linkDensity"]=linkdensity
# Adding average degree
avgdegree = 2*ne/nn
v["avgDegree"]=avgdegree
# Adding number of leaves
nl = numberLeaves(G)
v["numLeafs"]=nl
# Adding histogram of the nodes degree
v["histDegree0"]=0
v["histDegree1"]=0
v["histDegree2"]=0
v["histDegree3"]=0
v["histDegree4"]=0
histDegree=nx.degree_histogram(G)
v["histDegree0"]=histDegree[0]
if len(histDegree)>1:
v["histDegree1"]=histDegree[1]
if len(histDegree)>2:
v["histDegree2"]=histDegree[2]
if len(histDegree)>3:
v["histDegree3"]=histDegree[3]
if len(histDegree)>4:
v["histDegree4"]=histDegree[4]
# Adding sMetric
v["sMetric"]= metric(G)
# Adding graph energy
energ = graphEnergy (G)
v["graphEnergy"]=energ
# Adding average of the average neighboring degrees of all nodes
av = averageNeighDegree(G)
v["averageNeighDegree"]=av
# Adding average of closeness over all nodes
v["averageCloseness"]=average_closeness(G)
# Adding pearson coefficient for the degree sequence of all edges of the graph
pearson = nx.degree_pearson_correlation_coefficient(G)
if np.isnan(pearson):
pearson = 0
v["pearson"]=pearson
# Adding rich club metric for all nodes with a degree larger than 1
rc=richClub(G)
v["richClub"]=rc
# Adding algebraic connectivity, i.e. the second smallest eigenvalue of the Laplacian
algConnect = nx.laplacian_spectrum(G)
algConnect = list(algConnect)
algConnect = sorted(algConnect)
v["algConnect"]=algConnect[1]
# Adding diameter of the graph
if nx.is_connected(G):
diam = nx.diameter(G)
else:
diam="?"
v["diameter"]=diam
# Adding average shortest path
if nx.is_connected(G):
avgShortestPath=nx.average_shortest_path_length(G)
else:
avgShortestPath="?"
v["avgShortPath"]=avgShortestPath
# Adding graph radius
if nx.is_connected(G):
rad = nx.radius(G)
else:
rad="?"
v["graphRadius"]=rad
return v
# MAIN
"""G=nx.Graph()
nodes = [(2,7),(6,4),(18,5),(28,2),(17,5),(12,19)]
edges = [((2,7),(6,4)),((2,7),(18,5)),((6,4),(28,2)),((28,2),(17,5)),((18,5),(12,19))]
G.add_nodes_from(nodes)
G.add_edges_from(edges)
v=creationVecteur(G)
print(v)"""