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GCE.py
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GCE.py
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__author__ = 'wangting'
import networkx as nx
from PyCommDete import *
from PyCommDete import deal_cliques
from inputs.formal_edgelist import *
from socket import gethostname
hn=gethostname()
exec("from config.%s import *" % hn)
def find_maximum_clique_GCE(network):
# find the maximum cliques in network C, clique's nodes are over 4.
# http://networkx.lanl.gov/reference/algorithms.clique.html
cl = [x for x in nx.find_cliques(network)]
cl_over_4 = filter(lambda x:len(x)>=4, cl)
seeds = deal_seeds_GCE(cl_over_4)
# seeds = downsides_seeds(seeds,2)
# print "seeds:\n", seeds
print "number of seeds for computing: ", len(seeds)
return seeds
def deal_seeds_GCE(cliques):
print cliques
cliques.sort(key=lambda x:len(x), reverse=True)
print "after sorted: ",cliques
print "before num: ",len(cliques)
len_cli = len(cliques)
if len_cli > 2:
results = [cliques[0],cliques[1]]
else:
return cliques
def inter_perception(seed,non_seed):
inter_nodes = set(seed).intersection(set(non_seed))
percent = float(len(inter_nodes)) / float(len(non_seed))
return percent
for i in range(2,len_cli):
count = 0
for j in results:
if inter_perception(j,cliques[i]) >= 1-cch_threshold:
count += 1
if count >= 2:
break
if count < 2:
results.append(cliques[i])
print "befor deal_cliques: ", len(results)
results = deal_cliques(results)
print "after deal_cliques: ",len(results)
#results = downsides_seeds(results,2)
#print "after downside to ave: ",len(results)
return results
def compare_distance(one, results):
for y in results:
delta = distance_abandon(y.nodes(),one)
if delta < delta_threshold:
return True
return False
def get_all_nature_community_GCE(cliques):
global process_num
if len(cliques) > process_num:
results = get_nature_community_N_GCE(cliques[:process_num])
cliques_iter = cliques[process_num+1:]
while len(cliques_iter)!=0:
tem_seeds = []
# count = 0
if len(cliques_iter) > process_num:
i = 0
while i<len(cliques_iter) and len(tem_seeds)<process_num:
if compare_distance(cliques_iter[i], results):
tem_seeds.append(cliques_iter[i])
i += 1
x = cliques_iter[i]
count = cliques.index(x)
if len(tem_seeds) == process_num:
results += get_nature_community_N_GCE(tem_seeds)
cliques_iter = cliques_iter[count:]
else:
results += get_nature_community_N_GCE(tem_seeds)
cliques_iter = []
print "after for computing seeds num:___________________", len(cliques_iter)
else:
results += get_nature_community_N_GCE(cliques_iter)
cliques_iter = []
else:
results = get_nature_community_N_GCE(cliques)
communities = results
i = 0
for x in communities:
print "i = ",i,x.nodes()
i = i+1
print "finish get all communities"
communities = deal_communities(communities)
print "complete deal_communities"
return communities
def distance_abandon(comm,seed):
jointed = set(comm).intersection(set(seed))
distance = float(len(jointed)) / float(min(len(seed), len(comm)))
#print "distance:", distance
return distance
def get_nature_community_N_GCE(cliques):
# print "len of pool input cliques__________:", len(cliques)
pool_result = []
global process_num
if len(cliques) < process_num:
process_num = len(cliques)
# print "process_num: ", process_num
pool = Pool(process_num)
group_list = split_list(cliques, process_num)
for i in range(1, len(group_list), 2):
group_list[i].reverse()
for i in range(process_num):
args=[]
for gr in group_list:
if len(gr)>i:
args.append(gr[i])
args=(args,)
# args=([cliques[j] for j in range(cli_len) if j%process_num==i],)
print "args__________", args
pool_result.append(pool.apply_async(process_f,args))
pool.close()
pool.join()
communities=[]
for x in pool_result:
communities = communities+x.get()
print "finish process of N nature community___________________"
return communities
def distance_percent_non_embedded(comm1, comm2):
jointed = set(comm1).intersection(set(comm2))
distance = 1 - float(len(jointed)) / float(min(len(comm1), len(comm2)))
# print "distance:", distance
return distance
def deal_communities_with_distance(communities):
le = len(communities)
bm = [0]*le
result = []
for i in range(le):
for j in range(i+1, le):
x = communities[i]
y = communities[j]
dis = distance_percent_non_embedded(x, y)
def is_subset(a,b):
if len(a)>len(b):
return False
for x in a:
if x not in b:
return False
return True
if dis < dis_threshold:
if is_subset(y,x):
bm[j]=1
break
elif is_subset(x,y):
bm[i] = 1
break
else: #other
if len(x) >= len(y):
bm[j] = 1
break
else:
bm[i] = 1
break
for x in range(len(bm)-1, -1, -1):
if bm[x]:
communities.pop(x)
for x in result:
communities.append(x)
if len(communities) != le:
return deal_communities_with_distance(communities)
else:
return communities
if __name__ == "__main__":
import time
start = time.time()
from common.input_process import input_type_fun
C = input_type_fun(input_type)
seeds = find_maximum_clique_GCE(C)
print "length of seeds: ", len(seeds)
print "seeds: ", seeds
communities = get_all_nature_community_GCE(seeds)
print "commplete get_all_nature_community"
results = deal_communities_with_distance(communities)
f = file(base +'/evaluations/mutual3/result_GCE.dat', 'w+')
for line in results:
content = " ".join([str(x) for x in line])
f.write(content)
f.write('\n')
f.close()
# write results in file for evaluating
# import pickle
# pickle.dump(results, f)
print "----------------------------------The detection result is: \n"
print "all communities: "
i = 1
for x in results:
print "i = ", i, ":" , sorted(x.nodes())
i += 1
overlapping_nodes = set([])
commu = [set(x) for x in results]
for x in commu:
for y in commu[commu.index(x)+1:]:
temp = x.intersection(y)
overlapping_nodes = overlapping_nodes.union(temp)
print "overlapping nodes are: ", sorted(list(overlapping_nodes)), "\n NOO is: ", len(overlapping_nodes)
end = time.time() - start
print "total time is: ", end