def out_net_commudet(dbname, colname, name):
# transform the largest component to undirect for community detection
# G = nt.load_beh_network(dbname, colname)
G = nt.load_beh_network(dbname, colname)
nx.write_edgelist(G, "data/net.data")
# G = nx.read_edgelist("data/net.data")
GC = nt.get_gaint_comp(G)
# nt.net_statis(GC)
# GCG = GC.to_undirected()
# nt.net_statis(GCG)
nx.write_edgelist(GC, "data/comp.data")
print GC.number_of_selfloops()
out_net(GC, name)
def plot_communty(dbname, colname, name, commline):
G = nt.load_network(dbname, colname)
GC = nt.get_gaint_comp(G)
id_map = pickle.load(open("data/" + name + "-id.pick", "r"))
nodelist = list()
fr = open("data/" + name + "-fc_best.groups", "r")
line = ""
while commline not in line:
line = fr.readline()
print line
print "----------------------------"
for line in fr.readlines():
if "GROUP" in line:
break
else:
nodelist.append(id_map[int(line.strip())])
print "node size:", str(len(nodelist))
# plot.network_top(GC.subgraph(nodelist))
pickle.dump(nodelist, open("data/" + name + "uid.pick", "w"))
return GC.subgraph(nodelist)