def could_be_isomorphic(G1, G2):
"""Returns False if graphs are definitely not isomorphic.
True does NOT guarantee isomorphism.
Parameters
----------
G1, G2 : graphs
The two graphs G1 and G2 must be the same type.
Notes
-----
Checks for matching degree, triangle, and number of cliques sequences.
"""
# Check global properties
if G1.order() != G2.order(): return False
# Check local properties
d1 = G1.degree()
t1 = nx.triangles(G1)
c1 = nx.number_of_cliques(G1)
props1 = [[d, t1[v], c1[v]] for v, d in d1]
props1.sort()
d2 = G2.degree()
t2 = nx.triangles(G2)
c2 = nx.number_of_cliques(G2)
props2 = [[d, t2[v], c2[v]] for v, d in d2]
props2.sort()
if props1 != props2:
return False
# OK...
return True
def could_be_isomorphic(G1,G2):
"""Returns False if graphs are definitely not isomorphic.
True does NOT guarantee isomorphism.
Parameters
----------
G1, G2 : graphs
The two graphs G1 and G2 must be the same type.
Notes
-----
Checks for matching degree, triangle, and number of cliques sequences.
"""
# Check global properties
if G1.order() != G2.order(): return False
# Check local properties
d1 = G1.degree()
t1=nx.triangles(G1)
c1=nx.number_of_cliques(G1)
props1 = [[d, t1[v], c1[v]] for v, d in d1]
props1.sort()
d2=G2.degree()
t2=nx.triangles(G2)
c2=nx.number_of_cliques(G2)
props2 = [[d, t2[v], c2[v]] for v, d in d2]
props2.sort()
if props1 != props2:
return False
# OK...
return True
def graph_could_be_isomorphic(G1,G2):
"""
Returns False if graphs G1 and G2 are definitely not isomorphic.
True does NOT garantee isomorphism.
Checks for matching degree, triangle, and number of cliques sequences.
"""
# Check global properties
if G1.order() != G2.order(): return False
# Check local properties
d1=G1.degree(with_labels=True)
t1=networkx.triangles(G1,with_labels=True)
c1=networkx.number_of_cliques(G1,with_labels=True)
props1=[ [d1[v], t1[v], c1[v]] for v in d1 ]
props1.sort()
d2=G2.degree(with_labels=True)
t2=networkx.triangles(G2,with_labels=True)
c2=networkx.number_of_cliques(G2,with_labels=True)
props2=[ [d2[v], t2[v], c2[v]] for v in d2 ]
props2.sort()
if props1 != props2:
# print props1
# print props2
return False
# OK...
return True
def graphical_features(train_data, test_data):
''' Compute some structural features on the graph obtained from the questions '''
df = pd.concat([train_data[['qid1', 'qid2']], test_data[['qid1', 'qid2']]], axis = 0, ignore_index=True)
g = nx.Graph()
g.add_nodes_from(df.qid1)
edges = list(df[["qid1", "qid2"]].to_records(index=False))
g.add_edges_from(edges)
g.remove_edges_from(g.selfloop_edges())
print('Get kcore dict')
kcore_dict = nx.core_number(g)
print('Get centrality dict')
centrality_dict = nx.degree_centrality(g)
print('Get closeness dict')
closeness_dict = nx.closeness_centrality(g)
print('Get cliques dict')
cliques_dict = nx.number_of_cliques(g)
return np.array([(min(kcore_dict[qid1], kcore_dict[qid2]), max(kcore_dict[qid1], kcore_dict[qid2]),
min(centrality_dict[qid1], centrality_dict[qid2]), max(centrality_dict[qid1], centrality_dict[qid2]),
min(closeness_dict[qid1], closeness_dict[qid2]), max(closeness_dict[qid1], closeness_dict[qid2]),
min(cliques_dict[qid1], cliques_dict[qid2]), max(cliques_dict[qid1], cliques_dict[qid2]))
for qid1, qid2 in zip(train_data.qid1, train_data.qid2)]), \
np.array([(min(kcore_dict[qid1], kcore_dict[qid2]), max(kcore_dict[qid1], kcore_dict[qid2]),
min(centrality_dict[qid1], centrality_dict[qid2]), max(centrality_dict[qid1], centrality_dict[qid2]),
min(closeness_dict[qid1], closeness_dict[qid2]), max(closeness_dict[qid1], closeness_dict[qid2]),
min(cliques_dict[qid1], cliques_dict[qid2]), max(cliques_dict[qid1], cliques_dict[qid2]))
for qid1, qid2 in zip(test_data.qid1, test_data.qid2)])
def find_cliques(self):
''' Function finds the number of cliques to which each vertex belongs.
Generates a separate list for each chain.'''
self.cliques = []
for chain in self.chains:
# for each chain generate a dictionary of the number of cliques
# to which each atom in the chain belongs
self.cliques.append(nx.number_of_cliques(chain))
def find_cliques(self):
""" Function finds the number of cliques to which each vertex belongs.
Generates a separate list for each chain."""
self.cliques = []
for chain in self.chains:
# for each chain generate a dictionary of the number of cliques
# to which each atom in the chain belongs
self.cliques.append(nx.number_of_cliques(chain))
def nodes_number_of_cliques(self):
"""
Parameters
----------
Returns
-------
NxGraph: Graph object
Examples
--------
>>>
"""
if self.is_directed:
return nx.number_of_cliques(self._graph.to_undirected())
else:
return nx.number_of_cliques(self._graph)
def hash_nxgraph(g: nx.Graph):
t = nx.triangles(g)
c = nx.number_of_cliques(g)
ele = nx.get_node_attributes(g, 'symbol')
dv = g.degree
props = [(dv[v], t[v], c[v], ele[v]) for v in g]
props.sort()
s = str(props)
return hashstr2int(s)
def calculate_undir_centralities(self, G, U, suffix):
''' Calculate centralities in undirected graph and add them as node attributes '''
nx.set_node_attributes(G, nx.degree_centrality(U),
'degree_%s' % suffix)
nx.set_node_attributes(G, nx.node_clique_number(U),
'clique_number_%s' % suffix)
nx.set_node_attributes(G, nx.number_of_cliques(U),
'num_of_cliques_%s' % suffix)
return G
def props_for_hash(self):
"""
see https://stackoverflow.com/questions/46999771/
use with caution...
"""
t = nx.triangles(self.graph)
c = nx.number_of_cliques(self.graph)
ele = nx.get_node_attributes(self.graph, 'symbol')
dv = self.graph.degree
props = [(dv[v], t[v], c[v], ele[v]) for v in self.graph]
props.sort()
return props
def test_number_of_cliques(self):
G = self.G
assert nx.graph_number_of_cliques(G) == 5
assert nx.graph_number_of_cliques(G, cliques=self.cl) == 5
assert nx.number_of_cliques(G, 1) == 1
assert list(nx.number_of_cliques(G, [1]).values()) == [1]
assert list(nx.number_of_cliques(G, [1, 2]).values()) == [1, 2]
assert nx.number_of_cliques(G, [1, 2]) == {1: 1, 2: 2}
assert nx.number_of_cliques(G, 2) == 2
assert (nx.number_of_cliques(G) ==
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert (nx.number_of_cliques(G, nodes=list(G)) ==
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert (nx.number_of_cliques(G, nodes=[2, 3, 4]) ==
{2: 2, 3: 1, 4: 2})
assert (nx.number_of_cliques(G, cliques=self.cl) ==
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert (nx.number_of_cliques(G, list(G), cliques=self.cl) ==
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
def test_number_of_cliques(self):
G=self.G
assert_equal(nx.graph_number_of_cliques(G),5)
assert_equal(nx.graph_number_of_cliques(G,cliques=self.cl),5)
assert_equal(nx.number_of_cliques(G,1),1)
assert_equal(list(nx.number_of_cliques(G,[1]).values()),[1])
assert_equal(list(nx.number_of_cliques(G,[1,2]).values()),[1, 2])
assert_equal(nx.number_of_cliques(G,[1,2]),{1: 1, 2: 2})
assert_equal(nx.number_of_cliques(G,2),2)
assert_equal(nx.number_of_cliques(G),
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert_equal(nx.number_of_cliques(G,nodes=G.nodes()),
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert_equal(nx.number_of_cliques(G,nodes=[2,3,4]),
{2: 2, 3: 1, 4: 2})
assert_equal(nx.number_of_cliques(G,cliques=self.cl),
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
assert_equal(nx.number_of_cliques(G,G.nodes(),cliques=self.cl),
{1: 1, 2: 2, 3: 1, 4: 2, 5: 1,
6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1})
def cliques_strategy(G, num_seeds):
''' Picks the top nodes based on number of cliques per node
Args:
G -- the input graph
num_seeds -- the number of seed nodes to select
Returns: list of output nodes based on the cliques
'''
centralities_dict = nx.number_of_cliques(G)
sorted_centralities = nlargest(num_seeds,
centralities_dict.items(),
key=operator.itemgetter(1))
node_keys = [i[0] for i in sorted_centralities]
return node_keys
def compute_node_measures(ntwk, calculate_cliques=False):
"""
These return node-based measures
"""
iflogger.info('Computing node measures:')
measures = {}
iflogger.info('...Computing degree...')
measures['degree'] = np.array(list(ntwk.degree().values()))
iflogger.info('...Computing load centrality...')
measures['load_centrality'] = np.array(
list(nx.load_centrality(ntwk).values()))
iflogger.info('...Computing betweenness centrality...')
measures['betweenness_centrality'] = np.array(
list(nx.betweenness_centrality(ntwk).values()))
iflogger.info('...Computing degree centrality...')
measures['degree_centrality'] = np.array(
list(nx.degree_centrality(ntwk).values()))
iflogger.info('...Computing closeness centrality...')
measures['closeness_centrality'] = np.array(
list(nx.closeness_centrality(ntwk).values()))
# iflogger.info('...Computing eigenvector centrality...')
# measures['eigenvector_centrality'] = np.array(nx.eigenvector_centrality(ntwk, max_iter=100000).values())
iflogger.info('...Computing triangles...')
measures['triangles'] = np.array(list(nx.triangles(ntwk).values()))
iflogger.info('...Computing clustering...')
measures['clustering'] = np.array(list(nx.clustering(ntwk).values()))
iflogger.info('...Computing k-core number')
measures['core_number'] = np.array(list(nx.core_number(ntwk).values()))
iflogger.info('...Identifying network isolates...')
isolate_list = nx.isolates(ntwk)
binarized = np.zeros((ntwk.number_of_nodes(), 1))
for value in isolate_list:
value = value - 1 # Zero indexing
binarized[value] = 1
measures['isolates'] = binarized
if calculate_cliques:
iflogger.info('...Calculating node clique number')
measures['node_clique_number'] = np.array(
list(nx.node_clique_number(ntwk).values()))
iflogger.info('...Computing number of cliques for each node...')
measures['number_of_cliques'] = np.array(
list(nx.number_of_cliques(ntwk).values()))
return measures
def compute_node_measures(ntwk, calculate_cliques=False):
"""
These return node-based measures
"""
iflogger.info('Computing node measures:')
measures = {}
iflogger.info('...Computing degree...')
measures['degree'] = np.array(list(ntwk.degree().values()))
iflogger.info('...Computing load centrality...')
measures['load_centrality'] = np.array(
list(nx.load_centrality(ntwk).values()))
iflogger.info('...Computing betweenness centrality...')
measures['betweenness_centrality'] = np.array(
list(nx.betweenness_centrality(ntwk).values()))
iflogger.info('...Computing degree centrality...')
measures['degree_centrality'] = np.array(
list(nx.degree_centrality(ntwk).values()))
iflogger.info('...Computing closeness centrality...')
measures['closeness_centrality'] = np.array(
list(nx.closeness_centrality(ntwk).values()))
# iflogger.info('...Computing eigenvector centrality...')
# measures['eigenvector_centrality'] = np.array(nx.eigenvector_centrality(ntwk, max_iter=100000).values())
iflogger.info('...Computing triangles...')
measures['triangles'] = np.array(list(nx.triangles(ntwk).values()))
iflogger.info('...Computing clustering...')
measures['clustering'] = np.array(list(nx.clustering(ntwk).values()))
iflogger.info('...Computing k-core number')
measures['core_number'] = np.array(list(nx.core_number(ntwk).values()))
iflogger.info('...Identifying network isolates...')
isolate_list = nx.isolates(ntwk)
binarized = np.zeros((ntwk.number_of_nodes(), 1))
for value in isolate_list:
value = value - 1 # Zero indexing
binarized[value] = 1
measures['isolates'] = binarized
if calculate_cliques:
iflogger.info('...Calculating node clique number')
measures['node_clique_number'] = np.array(
list(nx.node_clique_number(ntwk).values()))
iflogger.info('...Computing number of cliques for each node...')
measures['number_of_cliques'] = np.array(
list(nx.number_of_cliques(ntwk).values()))
return measures
print str(" ")
print 'Ο αριθμός κλίκας (το μέγεθος της μεγαλύτερης κλίκας) του G είναι:', nx.graph_clique_number(
G)
# print 'The clique number (size of the largest clique) for G is:', nx.graph_clique_number(G)
# print sorted(nx.connected_components(G), key = len, reverse=True)
print str(" ")
print 'Το λεξικό των κλικών που περιέχουν κάθε κόμβο είναι:'
# print 'The dictionary of the lists of cliques containing each node:'
print nx.cliques_containing_node(G)
print str(" ")
print 'Το λεξικό του πλήθους κλικών που περιέχουν κάθε κόμβο είναι:'
# print 'The dictionary of the numbers of maximal cliques for each node:'
print nx.number_of_cliques(G)
print str(" ")
print 'Το λεξικό του μεγέθους των μεγαλύτερων κλικών που περιέχουν κάθε κόμβο είναι:'
# print 'The dictionary of the sizes of the largest maximal cliques containing each given node:'
print nx.node_clique_number(G)
print str(" ")
maxclique = [
clq for clq in nx.find_cliques(G) if len(clq) == nx.graph_clique_number(G)
]
nodes = [n for clq in maxclique for n in clq]
H = G.subgraph(nodes)
# print H.edges()
#### ΣΧΕΔΙΑΣΜΟΣ ΚΛΙΛΩΝ ΜΕΣΑ ΣΕ ΠΕΡΙΒΑΛΛΟΜΕΝΕΣ ΧΡΩΜΑΤΙΣΜΕΝΕΣ ΠΕΡΙΟΧΕΣ
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import networkx as nx
# opciok: https://networkx.github.io/documentation/stable/reference/index.html
G = nx.connected_caveman_graph(4, 7)
print(nx.number_of_cliques(G))
for clique in nx.find_cliques(G):
print(clique)
print("\n")
print(nx.degree_centrality(G))
print("\n")
print(nx.edge_betweenness_centrality(G))
print("\n")
degree_sequence = sorted(nx.degree(G).values(), reverse=True)
print(degree_sequence)
# -*- coding: utf-8 -*-
"""
AFRS - Trabalho 4
Author: Gonçalo Peres
Date: 2019/02/02
"""
import networkx as nx
g = nx.read_gml('dolphins.gml')
clique = nx.number_of_cliques(g)
print(clique)
@author: Administrator
"""
import networkx as nx
import matplotlib.pyplot as plt
from nx_graph_build import build_G
import community
#G = nx.read_gml(r"C:\Users\Administrator\Desktop\dolphins.gml")
G = build_G()
print(nx.number_of_nodes(G))
print(nx.number_of_edges(G))
#cliques 小圈子， nx.number_of_cliques(G, nodes = 'TR77') 返node所处的圈子位置。
#nx.number_of_cliques(G) 返回所有节点和所在的圈子，以{node：num} 字典的形式。
print(nx.number_of_cliques(G, nodes = 'TR77'))
#list of k-cliques in the network. each element contains the nodes that consist the clique.
klist = list(nx.community.k_clique_communities(G,3))
print(len(klist))
#小圈子中nx.community.k_clique_communities(G,k) k个点要求是相互有联系，既之前都有线相连， 多个小圈子组成的社区
#通过设置不同的k值，圈出的社区数量也会随之变化
#for k in range(3,6):
# klist_new = list(nx.community.k_clique_communities(G,k))
# print(len(klist_new))
#去除在社区中的节点，留下不属于任何社区的节点
aa = list(G.nodes())
list_1 = []
for item in klist:
list_1.extend(item)
print "Compute number of maximal ciiques" print "-------------------------------------" graphNumberOfCliques = nx.graph_number_of_cliques(G, cliques) print graphNumberOfCliques print "-------------------------------------" print "Compute size of largest maximal clique containing a given node" print "-------------------------------------" maximalCliqueSizePerNode = nx.node_clique_number(G) print maximalCliqueSizePerNode print "-------------------------------------" print "Compute number of maximal cliques for each node" print "-------------------------------------" noOfMaximalCliquesPerNode = nx.number_of_cliques(G) print noOfMaximalCliquesPerNode print "-------------------------------------" print "Compute list of cliques containing a given node" print "-------------------------------------" lcliques = nx.cliques_containing_node(G) print lcliques print "-------------------------------------" print "Writing data into global file" print "-------------------------------------" globalCliqueFile = 'data/globalCliqueFile.csv' mode = ''; if os.path.isfile(globalCliqueFile):
G.add_edges_from(edgeTuples)
return G
#Read in data
nodesOne = pd.read_csv('data/got-s1-nodes.csv', low_memory=False)
edgesOne = pd.read_csv('data/got-s1-edges.csv', low_memory=False)
G = create_graph(nodesOne, edgesOne)
nx.degree(G)
nx.modularity_matrix(G)
nx.density(G)
list(nx.find_cliques(G))
nx.number_of_cliques(G)
nx.clustering(G)
nx.eigenvector_centrality(G)
nx.number_of_isolates(G)
nx.isolates(G)
nx.pagerank(G)
nx.shortest_path(G)
print lvl2
print str(" ")
print "Ο αριθμός κλίκας (το μέγεθος της μεγαλύτερης κλίκας) του G είναι:", nx.graph_clique_number(G)
# print 'The clique number (size of the largest clique) for G is:', nx.graph_clique_number(G)
# print sorted(nx.connected_components(G), key = len, reverse=True)
print str(" ")
print "Το λεξικό των κλικών που περιέχουν κάθε κόμβο είναι:"
# print 'The dictionary of the lists of cliques containing each node:'
print nx.cliques_containing_node(G)
print str(" ")
print "Το λεξικό του πλήθους κλικών που περιέχουν κάθε κόμβο είναι:"
# print 'The dictionary of the numbers of maximal cliques for each node:'
print nx.number_of_cliques(G)
print str(" ")
print "Το λεξικό του μεγέθους των μεγαλύτερων κλικών που περιέχουν κάθε κόμβο είναι:"
# print 'The dictionary of the sizes of the largest maximal cliques containing each given node:'
print nx.node_clique_number(G)
print str(" ")
maxclique = [clq for clq in nx.find_cliques(G) if len(clq) == nx.graph_clique_number(G)]
nodes = [n for clq in maxclique for n in clq]
H = G.subgraph(nodes)
# print H.edges()
#### ΣΧΕΔΙΑΣΜΟΣ ΚΛΙΛΩΝ ΜΕΣΑ ΣΕ ΠΕΡΙΒΑΛΛΟΜΕΝΕΣ ΧΡΩΜΑΤΙΣΜΕΝΕΣ ΠΕΡΙΟΧΕΣ
def get_graph(Mat_D, Threshold, percentageConnections=False, complet=False):
import scipy.io as sio
import numpy as np
import networkx as nx
import pandas as pd
import os
Data = sio.loadmat(Mat_D)
matX = Data['Correlation'] #[:tamn,:tamn]
labels = Data['labels']
print(np.shape(matX))
print(np.shape(labels))
print(np.min(matX), np.max(matX))
if percentageConnections:
if percentageConnections > 0 and percentageConnections < 1:
for i in range(-100, 100):
per = np.sum(matX > i / 100.) / np.size(matX)
if per <= Threshold:
Threshold = i / 100.
break
print(Threshold)
else:
print('The coefficient is outside rank')
#Lista de conexion del grafo
row, col = np.shape(matX)
e = []
for i in range(1, row):
for j in range(i):
if complet:
e.append((labels[i], labels[j], matX[i, j]))
else:
if matX[i, j] > Threshold:
e.append((labels[i], labels[j], matX[i, j]))
print(np.shape(e)[0], int(((row - 1) * row) / 2))
#Generar grafo
G = nx.Graph()
G.add_weighted_edges_from(e)
labelNew = list(G.nodes)
#Metricas por grafo (ponderados)
Dpc = nx.degree_pearson_correlation_coefficient(G, weight='weight')
cluster = nx.average_clustering(G, weight='weight')
#No ponderados
estra = nx.estrada_index(G)
tnsity = nx.transitivity(G)
conNo = nx.average_node_connectivity(G)
ac = nx.degree_assortativity_coefficient(G)
#Metricas por nodo
tam = 15
BoolCenV = False
BoolLoad = False
alpha = 0.1
beta = 1.0
katxCN = nx.katz_centrality_numpy(G,
alpha=alpha,
beta=beta,
weight='weight')
bcen = nx.betweenness_centrality(G, weight='weight')
av_nd = nx.average_neighbor_degree(G, weight='weight')
ctr = nx.clustering(G, weight='weight')
ranPaN = nx.pagerank_numpy(G, weight='weight')
Gol_N = nx.hits_numpy(G)
Dgc = nx.degree_centrality(G)
cl_ce = nx.closeness_centrality(G)
cluster_Sq = nx.square_clustering(G)
centr = nx.core_number(G)
cami = nx.node_clique_number(G)
camiN = nx.number_of_cliques(G)
trian = nx.triangles(G)
colorG = nx.greedy_color(G)
try:
cenVNum = nx.eigenvector_centrality_numpy(G, weight='weight')
tam = tam + 1
BoolCenV = True
except TypeError:
print(
"La red es muy pequeña y no se puede calcular este parametro gil")
except:
print('NetworkXPointlessConcept: graph null')
if Threshold > 0:
carga_cen = nx.load_centrality(G, weight='weight') #Pesos positivos
BoolLoad = True
tam = tam + 1
#katxC=nx.katz_centrality(G, alpha=alpha, beta=beta, weight='weight')
#cenV=nx.eigenvector_centrality(G,weight='weight')
#cenV=nx.eigenvector_centrality(G,weight='weight')
#Golp=nx.hits(G)
#Gol_si=nx.hits_scipy(G)
#ranPa=nx.pagerank(G, weight='weight')
#ranPaS=nx.pagerank_scipy(G, weight='weight')
matrix_datos = np.zeros((tam, np.shape(labelNew)[0]))
tam = 15
print(np.shape(matrix_datos))
lim = np.shape(labelNew)[0]
for i in range(lim):
roi = labelNew[i]
#print(roi)
matrix_datos[0, i] = katxCN[roi]
matrix_datos[1, i] = bcen[roi]
matrix_datos[2, i] = av_nd[roi]
matrix_datos[3, i] = ctr[roi]
matrix_datos[4, i] = ranPaN[roi]
matrix_datos[5, i] = Gol_N[0][roi]
matrix_datos[6, i] = Gol_N[1][roi]
matrix_datos[7, i] = Dgc[roi]
matrix_datos[8, i] = cl_ce[roi]
matrix_datos[9, i] = cluster_Sq[roi]
matrix_datos[10, i] = centr[roi]
matrix_datos[11, i] = cami[roi]
matrix_datos[12, i] = camiN[roi]
matrix_datos[13, i] = trian[roi]
matrix_datos[14, i] = colorG[roi]
if BoolCenV:
matrix_datos[15, i] = cenVNum[roi]
tam = tam + 1
if BoolLoad:
matrix_datos[16, i] = carga_cen[roi]
tam = tam + 1
#matrix_datos[0,i]=katxC[roi]
#matrix_datos[2,i]=cenV[roi]
#matrix_datos[7,i]=Golp[0][roi]
#matrix_datos[9,i]=Gol_si[0][roi]
#matrix_datos[10,i]=Golp[1][roi]
#matrix_datos[12,i]=Gol_si[1][roi]
#matrix_datos[22,i]=ranPa[roi]
#matrix_datos[24,i]=ranPaS[roi]
FuncName = [
'degree_pearson_correlation_coefficient', 'average_clustering',
'estrada_index', 'transitivity', 'average_node_connectivity',
'degree_assortativity_coefficient', 'katz_centrality_numpy',
'betweenness_centrality', 'average_neighbor_degree', 'clustering',
'pagerank_numpy', 'hits_numpy0', 'hits_numpy1', 'degree_centrality',
'closeness_centrality', 'square_clustering', 'core_number',
'node_clique_number', 'number_of_cliques', 'triangles', 'greedy_color',
'eigenvector_centrality_numpy', 'load_centrality'
]
frame = pd.DataFrame(matrix_datos)
frame.columns = labelNew
frame.index = FuncName[6:tam]
Resul = os.getcwd()
out_data = Resul + '/graph_metrics.csv'
out_mat = Resul + '/graph_metrics_global.mat'
frame.to_csv(out_data)
sio.savemat(
out_mat, {
FuncName[0]: Dpc,
FuncName[1]: cluster,
FuncName[2]: estra,
FuncName[3]: tnsity,
FuncName[4]: conNo,
FuncName[5]: ac
})
return out_data, out_mat
print 'loading edges'
for i in temp1:
G.add_edge(i[0], i[1])
print 'loading nodes'
for i in temp2:
G.add_node(i[0])
print 'calc core number'
cn = nx.core_number(G)
print 'calc clique number'
kn = nx.node_clique_number(G)
print 'calc number of cliques'
kn2 = nx.number_of_cliques(G)
print 'calculating node measures'
for u, e in G.nodes_iter(data=True):
dr = str(nx.degree(G, u))
cl = str(nx.clustering(G, u))
#cc = str(nx.betweenness_centrality(G)[u])
ct = cn[u]
knu = kn[u]
kn2u = kn2[u]
#ks = str(nx.k_shell(G)[u])
f3.write(u + " " + dr + " " + cl + " " + str(ct) + " " + str(knu) + " " +
str(kn2u) + "\n")
f3.close()
print 'calc edge measures'
(i.to_undirected().number_of_edges() * 2 /i.to_undirected().number_of_nodes()) ** 2)
print 'Average number of neighbors'
print sum(nx.average_neighbor_degree(i.to_undirected()).values())
print 'Nodes'
print nx.number_of_nodes(i.to_undirected())
print 'edges'
print nx.number_of_edges(i.to_undirected())
print 'Density'
print nx.density(trem_nets.to_undirected())
print 'Number of cliques'
print len(nx.number_of_cliques(i.to_undirected()))
print 'Average Degree'
print 2.0 * (len(i.to_undirected().edges())) / len(i.to_undirected().nodes())
print 'Average Degree Assortativity'
print nx.degree_assortativity_coefficient(i.to_undirected())
# things for only connected graphs
ddi = max(nx.connected_component_subgraphs(ddi_nets.to_undirected()), key=len)
print 'Network Diameter'
nx.diameter(ddi.to_undirected())
print 'Shorest paths (sum)'
sum(nx.all_pairs_shortest_path_length(ddi_owl.to_undirected()).values()[0].values())
for articles in db.view('_all_docs'): ##search quoter in autors
j=articles['id']
if db[j]["Author"] == quoter: ##quoter is an author in database
for quot in db[j]["Quoters"]: #search author in qouters of quoter
if quot == Aut:
H.add_node(quot)
H.add_node(Aut)
H.add_edge(Aut, quoter)
nx.draw(H,pos=nx.spring_layout(H))
NumOfCliqes=nx.graph_clique_number(H)
print ("Clique number of the graph : ")
print (NumOfCliqes)
#
MaxCliques = nx.find_cliques(H)
print ("All maximal cliques: ")
print(list(MaxCliques))
##
node_clique_number=nx.node_clique_number(H)
print ("Size of the largest maximal clique containing each given node")
print (node_clique_number)
number_of_cliques=nx.number_of_cliques(H)
print ("Number of maximal cliques for each node.")
print (number_of_cliques)
