def read_graph():
'''
Reads the input network in networkx.
'''
if args.weighted:
G = nx.read_edgelist(args.input,
nodetype=int,
data=(('weight', float), ),
create_using=nx.DiGraph())
else:
G = nx.read_edgelist(args.input,
nodetype=int,
create_using=nx.DiGraph())
for edge in G.edges():
G[edge[0]][edge[1]]['weight'] = 1
if not args.directed:
G = G.to_undirected()
return G
import itertools
import operator
from networkx import nx
import matplotlib.pyplot as plt
import statistics
import community
import networkx as nx
G = nx.read_edgelist("30temple83Rel.txt")
totalcentra = nx.degree_centrality(G)
{k: v for k, v in sorted(totalcentra.items(), key=lambda item: item[1])}
for x in list(reversed(list(totalcentra)))[0:5]:
print (totalcentra[x])
window=window_size,
min_count=0,
sg=1,
workers=8,
iter=1)
model.wv.save(output)
return model.wv
#G0, Polyid, Polysynset, _,_,_ = generate_word_graph(True, False, False, 0)
#snap.SaveEdgeList(G0, "G0.txt", "")
#G1, Polyid, Polysynset, _,_,_ = generate_word_graph(True, False, False, 1)
#snap.SaveEdgeList(G1, "G1.txt", "")
#G2, Polyid, Polysynset, _,_,_ = generate_word_graph(True, False, False, 2)
#snap.SaveEdgeList(G2, "G2.txt", "")
G0 = nx.read_edgelist("G0.txt", nodetype=int)
for edge in G0.edges():
G0[edge[0]][edge[1]]['weight'] = 1.0
G1 = nx.read_edgelist("G1.txt", nodetype=int, create_using=nx.DiGraph())
for edge in G1.edges():
G1[edge[0]][edge[1]]['weight'] = 1.0
G2 = nx.read_edgelist("G2.txt", nodetype=int, create_using=nx.DiGraph())
for edge in G2.edges():
G2[edge[0]][edge[1]]['weight'] = 1.0
p = 100000
q = 1
walk_length = 20
window_size = 20
num_walks = 100
dimension = 50
G1 = node2vec.Graph(G1, False, p, q)
from networkx import nx
from random import randint
import json
input_folder = "data"
num_node_network = 10
path_random_network = input_folder+"/random_network_"+str(num_node_network)+".csv"
input_random_network = open(path_random_network)
graph = nx.read_edgelist(input_random_network, delimiter=',', create_using=nx.DiGraph(), nodetype=int, data=(('cost', float), ('capacity', float)))
# read configuration file with orders parameter for any order of our problem
orders_configuration_file = open("Config/orders_config.json").read()
orders_configuration_parameters = json.loads(orders_configuration_file)
# assign orders parameter into variable
min_capacity = orders_configuration_parameters["min_capacity"] # capacity minima ( = capacity truck)
min_len_shortest_path = orders_configuration_parameters["min_len_shortest_path"] # lunghezza minima shortest path
num_max_orders = orders_configuration_parameters["num_max_orders"] # numero massimo di ordini, solo se limited_num_orders = true
limited_num_orders = True # se = True => vengono salvati al max num_max_orders ordini
unique_sources = orders_configuration_parameters["unique_sources"] # se = True => i nodi partenza degli ordini saranno tutti diversi
nodes = graph.nodes()
orders = [] # lista di ordini
order_count = 0
for n1 in nodes:
tmp = list(nodes)
tmp.remove(n1)
for n2 in tmp:
if limited_num_orders and len(orders) == num_max_orders: # vengono fatti comunque dei cicli inutili, ma pazienza
write_score_file('SI', si_res_list)
write_score_file('SIR', sir_res_list)
if __name__ == "__main__":
import time
start = time.clock()
file_name = "../dataset/facebook_combined.txt"
#file_name = "../dataset/CA-GrQc_nor.txt" ;
#file_name = "../dataset/karate.txt" ;
#file_name = "../dataset/friendships-hamster_new.txt" ;
#file_name = "../dataset/hamster_full_new.txt" ;
#file_name = "../dataset/blog_edges.txt" ;
G = nx.read_edgelist(file_name)
seedList = [3]
run_num = 200
f = open(output_file, 'w')
f.close()
for seed in seedList:
f = open(output_file, 'a')
print >> f, "***** seed_num=", seed, "*****"
f.close()
print "***** seed_num=", seed, "*****"
my_run(G, seed, run_num)
f = open(output_file, 'a')
print >> f, "\n"
f.close()
def activate_nodes(fileN, p, beginRange, endRange, outFileName):
# READ GRAPH FROM FILE
g = nx.read_edgelist(fileN)
#g = nx.read_weighted_edgelist(fileN)
# SORT BY DEGREE
sorted(g.degree, key=lambda x: x[1], reverse=True)
# SET ATTRIBUTE 0 TO ALL NODES
attr = 0
nx.set_node_attributes(g, attr, 'attr')
att = nx.get_node_attributes(g, 'attr')
#print att
numNodes = 0
for k, v in att.items():
numNodes = numNodes + 1
#print("Key : {0}, Value : {1}".format(k, v))
#numNodes = number_of_nodes(g)
if (p == 0.3):
# RANGE ADJUST
a = 100 - beginRange
b = 100 - endRange
beginAtt = (a * numNodes) / 100
endAtt = (b * numNodes) / 100
# FINAL POSITIONS OF RANGE ON DICTIONARY
beginAtt = numNodes - beginAtt
endAtt = numNodes - endAtt
while (beginAtt <= endAtt):
att[att.keys()[beginAtt]] = 1
beginAtt = beginAtt + 1
#for k, v in att.items():
#print("Key : {0}, Value : {1}".format(k, v))
if (p == 0.2):
# RANGE ADJUST
a = 100 - beginRange
b = 100 - (endRange - 10)
beginAtt = (a * numNodes) / 100
endAtt = (b * numNodes) / 100
# FINAL POSITIONS OF RANGE ON DICTIONARY
beginAtt = numNodes - beginAtt
endAtt = numNodes - endAtt
while (beginAtt <= endAtt):
att[att.keys()[beginAtt]] = 1
beginAtt = beginAtt + 1
#for k, v in att.items():
#print("Key : {0}, Value : {1}".format(k, v))
if (p == 0.1):
# RANGE ADJUST
a = 100 - beginRange
b = 100 - (endRange - 20)
beginAtt = (a * numNodes) / 100
endAtt = (b * numNodes) / 100
# FINAL POSITIONS OF RANGE ON DICTIONARY
beginAtt = numNodes - beginAtt
endAtt = numNodes - endAtt
while (beginAtt <= endAtt):
att[att.keys()[beginAtt]] = 1
beginAtt = beginAtt + 1
#for k, v in att.items():
#print("Key : {0}, Value : {1}".format(k, v))
if (p == 0.05):
# RANGE ADJUST
a = 100 - beginRange
b = 100 - (endRange - 25)
beginAtt = (a * numNodes) / 100
endAtt = (b * numNodes) / 100
# FINAL POSITIONS OF RANGE ON DICTIONARY
beginAtt = numNodes - beginAtt
endAtt = numNodes - endAtt
while (beginAtt <= endAtt):
att[att.keys()[beginAtt]] = 1
beginAtt = beginAtt + 1
#for k, v in att.items():
#print("Key : {0}, Value : {1}".format(k, v))
nx.set_node_attributes(g, att, 'attr')
nx.write_edgelist(g, outFileName+".elist.txt", data=['attr'])
#att = nx.get_node_attributes(g, 'attr')
#for k, v in att.items():
# print("Key : {0}, Value : {1}".format(k, v))
measure_MI(g)
# Original data from http://www.sociopatterns.org/datasets/high-school-contact-and-friendship-networks/
import matplotlib.pyplot as plt
from networkx import nx
G = nx.read_edgelist('Facebook-known-pairs_data_2013_cleaned.csv', delimiter=',')
options = {'node_color': 'blue', 'node_size': 10, 'line_color': 'grey', 'linewidths': 0, 'width': 0.1}
plt.figure(figsize=(8.8, 3.5))
nx.draw_spring(G, **options)
plt.show()