PyCommDete.py

# -*- coding: utf8 -*-
from __future__ import division

import sys
import networkx as nx
import pickle
from random import random
from common.transform import split_list
from multiprocessing import Process, Pool
from inputs.formal_edgelist import *
#from SeedDrivenDete import *
from socket import gethostname

hn=gethostname()
exec("from config.%s import *" % hn)


if input_type==1:
	C = nx.read_gml(filelist[file_num])
elif input_type==2:
	C = nx.Graph(formal_edgelist(base +'/benchmark_LFR_OC_UU/network.dat'))
elif input_type==3:
	from inputs.friendster_dataset.friendster_graph import get_friendster_graph
	C = get_friendster_graph()

#print "nodes_______",len(C.nodes())
#print C.edges()
#print len(C.edges())
#exit()

len_C = len(C)
nodes_C = C.nodes()
degree_dict = C.degree()
betweenness_dict = nx.betweenness_centrality(C)

len_max = len_C
if len_C >= 1000:
	len_max = len_C*0.1


def get_maximum_cliques(network):
	# find the maximum cliques in network C, clique's nodes are over 2.
	# http://networkx.lanl.gov/reference/algorithms.clique.html
	cl = [x for x in nx.find_cliques(network)]
	cl_over_2 = filter(lambda x:len(x)>2, cl)
	print "_________len", len(cl_over_2)
#	seeds = deal_cliques(cl_over_2)
	seeds = cl_over_2
	print ":::::::::::",seeds


	seeds = downsides_seeds(seeds)

	print "seeds:\n", seeds
	print "number of seeds for computing: ", len(seeds)
	return seeds

def downsides_seeds(seeds,avg_type = 1):
	count_len = [len(x) for x in seeds]
	if avg_type == 0:
		MinSeedSize = 3
	elif avg_type == 1:
		MinSeedSize = 4
	elif avg_type == 2:
		MinSeedSize = sum(count_len)/len(seeds)
	elif avg_type ==3:
		ave = sum(count_len)/len(seeds)
		sum_tem = sum(map(lambda x:pow((ave-x),2),count_len))
		MinSeedSize = ave - pow(sum_tem/len(seeds),0.5)
#    cliques.sort(key=lambda x:len(x), reverse=True)
	seeds = [x for x in seeds if len(x) >= MinSeedSize]
	seeds.sort(key=lambda x:len(x), reverse = True)
	return seeds

def deal_cliques(cliques):
	new_cliques = deal_cliques_once(cliques)
	while new_cliques != cliques:
		cliques = new_cliques
		new_cliques = deal_cliques_once(cliques)
	return new_cliques

def deal_cliques_once(cliques):
	"""return a list containing cliques"""

	le = len(cliques)
	if le == 0:
		return []
	if le == 1:
		return list(cliques[0])

	# sort the list of lists based on the length of the list
	cliques.sort(key=lambda x:len(x), reverse=True)
	# merge the cliques if they are shared n-1 nodes
	result=[]
	current=[]
	le = len(cliques)
	bitmap=[0]*le

	while sum(bitmap) < le:
		for i, e in enumerate(bitmap):
			if e==0:
				current.append(i)
				bitmap[i] = 1
				break

		remain=[i for i,e in enumerate(bitmap) if e==0]


		def c_match(current_list, c):
			x = set(cliques[c])
			for i in current_list:
				y = set(cliques[i])
				if len(x) == len(y) and len(x.intersection(y)) == len(y)-1:
					return True
			return False

		for c in remain:
			if c_match(current, c):
				current.append(c)
				bitmap[c] = 1
		tmp=set()
		for x in current:
			tmp = tmp.union(cliques[x])
		result.append(tmp)
		current=[]
	return result

def get_neighbors(Graph):
	"""find subgraph's(or graph, type is Graph) neighbor nodes in original graph C"""
	flag={}
	def mark_true(x):
		t = C.neighbors(x)
		for x in t:
			flag[x]=1
	map(mark_true, Graph.nodes())
	for x in Graph.nodes():
		flag[x]=0

	return [x for x in nodes_C if flag.get(x)]

def get_fitness(Graph):
	"""compute the fitness of the graph"""
	if len(Graph.nodes()) == 1:
		return float(0)

	kin = 2 * len(Graph.edges())
	G_neighbors = get_neighbors(Graph)   # find G's neighbor nodes
	G_with_neighbors = G_neighbors + Graph.nodes()
	G_nei = nx.Graph(C.subgraph(G_with_neighbors))
	kout = len(G_nei.edges()) - len(Graph.edges())
	return  kin / pow((kin+kout), alpha)

#def get_fitness_v_max(Graph):
#	"""这有个问题，如果最大贡献度所对应的节点有多个怎么处理，是添加第一个最大的节点，还是所有的都添加
#	compute the fitness_v(dic, {node, fitness of adding vertex}), find fitness_v_max"""
#	G_neighbors = get_neighbors(Graph)
#	v_max_node=[[], float(-100)]
#	G_nodes = Graph.nodes()
#	#	vertex_list = [] # 添加一个初始值,将最大贡献度所对应的一些节点一起加入社区
#	if G_neighbors != []:
#		result = map(lambda x:[get_fitness(nx.Graph(C.subgraph((G_nodes+[x])))), x], G_neighbors)
#
#		result.sort(key=lambda x:x[0], reverse=True)
#		max_value = result[0][0]
#		nodelist = [x[1] for x in result if x[0]>=max_value]
#		v_max_node = [nodelist, max_value]
#
#	return v_max_node
#
#def get_fitness_v_community(Graph):
#	"""节点对社区的贡献度"""
#	r = get_fitness_v_max(Graph)
#	fitness_v_max_value = r[1]  #return (node_list, max_value)
#	fitness_v_community_value = fitness_v_max_value - get_fitness(Graph)
#	return (r[0], fitness_v_community_value)

def get_nature_community_short(Graph):
	if len(Graph) >= len_max:
		return C
	g_fitness = get_fitness(Graph)
	g_nodes=Graph.nodes()
	nei = get_neighbors(Graph)
	if len(nei) == 0:
		return Graph
	v_nei = [set(x) for x in map(C.neighbors, nei)]
	def get_connection(x):
		return set(g_nodes).intersection(x)
	v_con = map(get_connection, v_nei)
	v_all = []
	for i in range(len(nei)):
		temp = [nei[i], len(v_nei[i]), len(v_con[i]), float(len(v_con[i])/len(v_nei[i]))]
		v_all.append(temp)
	v_nei_max = 0
	v_con_max = 0
	v_con_nei_max = 0
	v_max = -1
	fitness_max = 0
	for v in v_all:
		if v[3] > v_con_nei_max:
			if v[1] > v_nei_max and v[2] > v_con_max:
				vg =  nx.Graph(C.subgraph(g_nodes + [v[0]]))
				incr_fitness = get_fitness(vg)-g_fitness
				if incr_fitness > 0 and incr_fitness > fitness_max:
					v_max = v[0]
					fitness_max = incr_fitness
					v_nei_max = v[1]
					v_con_max = v[2]
					v_con_nei_max = v[3]
			else:
				v_nei_max = v[1]
				v_con_max = v[2]
				v_con_nei_max = v[3]
				v_max = v[0]
	if v_max > 0:
		new_graph = nx.Graph(C.subgraph(g_nodes+[v_max]))
		return get_nature_community_short(new_graph)
	else:
		return Graph

def get_nature_community(Graph):
	if len(Graph) > len_max:
		return C
	g_fitness = get_fitness(Graph)
	g_nodes=Graph.nodes()

	v_fitness=[]
	nei = get_neighbors(Graph)

	if len(nei) == 0:
		return Graph

	for v in nei:
		vg =  nx.Graph(C.subgraph(g_nodes + [v]))
		incr_fitness = get_fitness(vg)-g_fitness

		if incr_fitness > 0:
			v_fitness.append([v, incr_fitness])

	if len(v_fitness) > 0:
		v_fitness.sort(key=lambda x:x[1], reverse=True)
		extends_list = [x[0] for x in v_fitness if x[1] >= v_fitness[0][1]]
#		extends_list = [v_fitness[0][0]]
		if len(extends_list) >0:
			new_graph = nx.Graph(C.subgraph(g_nodes+extends_list))
			return get_nature_community(new_graph)
		else:
			return Graph
	else:
		return Graph

def process_f(cli_list):
	lec = len_C
	r = map(get_nature_community_short, [nx.Graph(C.subgraph(c)) for c in cli_list])
#	print "origin len is", len(r)
	nr = filter(lambda x: len(x)<lec, r)
#	print "filter len is", len(nr)
	return r

def get_all_nature_community(cliques):
	pool_result = []
	global process_num
	if len(cliques) < process_num:
		process_num = len(cliques)

	pool = Pool(process_num)
	cli_len = len(cliques)

	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___________________"
	all_nodes = nodes_C
	comm_nodes=[]
	for x in communities:
		comm_nodes = comm_nodes + x.nodes()
	left_list = [x for x in all_nodes if x not in comm_nodes]

	while len(left_list)>0:
		single_seed_communities = get_single_seed_nature_community_once(left_list)
		communities = communities+single_seed_communities
		for comm in communities:
			for x in comm.nodes():
				if x in left_list:
					left_list.remove(x)

#	while len(left_list)>0:
#		seed_node = get_degree_max(left_list)
#		print "seed_node",seed_node
#		seed_clique = get_cliques(C, seed_node)
##		print "seed_clique", seed_clique
#		single_node_Graph = nx.Graph(C.subgraph(seed_clique))
##		single_node_Graph.add_node(seed_node)
#		single_node_Graph = get_nature_community_short(single_node_Graph)
#
#		sngn = single_node_Graph.nodes()
#		print "seed_community: ", sngn
#		communities.append(single_node_Graph)
#		for x in sngn:
#			if x in left_list:
#				left_list.remove(x)

	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 get_single_seed_nature_community_once(nodes):
	global process_num
	if len(nodes) >= process_num:
		tem_list = get_betweenness_max_num(nodes,process_num)
	else:
		tem_list = get_betweenness_max_num(nodes, len(nodes))
	from SeedDrivenDete import get_all_cliques_by_nodes
	cliques = get_all_cliques_by_nodes(C,tem_list)
	pool_result = []
	if len(cliques) < process_num:
		process_num = len(cliques)
	pool = Pool(process_num)
	cli_len = len(cliques)
	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 single seed process___________________"

	return communities


def deal_communities(communities):
	# if there are some communities are the same, than delete
	le = len(communities)
	cnode_len = len_C

	bm=[0]*le
	for i in range(le):
		if not bm[i]:
			for j in range(i+1,le):
				if compare_communities(communities[i].nodes(), communities[j].nodes()):
					bm[j] = 1

	for i in range(le-1,-1,-1):
		if bm[i] or len(communities[i])==cnode_len:
			communities.pop(i)

	# is_sub_graph
	def to_be_del(item, com):
		for x in com:
			if len(x)> len(item) and set(item).issubset(set(x.nodes())):
				return True

		return False

	bitmap=[0]*len(communities)
	for i in range(len(communities)):
		if to_be_del(communities[i].nodes(), communities):
			bitmap[i]=1
	for i in range(len(bitmap)-1, -1, -1):
		if bitmap[i]:
			communities.pop(i)
	return communities

def get_communities_overlapping_degree(community1, community2):
	nei1 = get_neighbors(community1)
	nei2 = get_neighbors(community2)
	val_o = get_overlapping_nodes(community1.nodes(), community2.nodes())
	val_m = get_merging_nodes(community1.nodes(), community2.nodes())
	if len(nei1)==0 and len(nei2)==0:
		cod = len(val_o) / len(val_m)
	else:
		cod = beta*len(val_o) / len(val_m) +\
			  (1-beta)*len(get_overlapping_nodes(nei1, nei2)) / len(get_merging_nodes(nei1, nei2))

	return cod

def compare_communities(community1_nodes, community2_nodes):
	len1 = len(community1_nodes)
	len2 = len(community2_nodes)

	if len1 != len2:
		return False

	i = 0
	while i < len1:
		if community1_nodes[i] != community2_nodes[i]:
			return False
		i = i + 1
	return True

def get_degree_max(nodes):
	max= -1
	node  = -1
	for x in nodes:
		if degree_dict[x] > max:
			max = degree_dict[x]
			node = x
	return node

def get_degree_max_num(nodes,k):
	sorted(nodes, key=lambda x:degree_dict[x])
	print "sorted nodes by degree",nodes
	return nodes[:k]

def get_betweenness_max_num(nodes,k):
	print "left nodes numner:_______", len(nodes)
	sorted(nodes, key=lambda x:betweenness_dict[x])
	print "sorted nodes by betweenness",nodes
	return nodes[:k]

def get_overlapping_nodes(community1_nodes, community2_nodes):
	return [x for x in community1_nodes if x in community2_nodes]
#
#	overlapping_nodes=[]
#	for x in community1_nodes:
#		if x in community2_nodes:
#			overlapping_nodes.append(x)
#	return overlapping_nodes

def get_merging_nodes(community1_nodes, community2_nodes):
#	return list(set(community1_nodes + community2_nodes))
	result=community1_nodes
	for x in community2_nodes:
		if x not in community1_nodes:
			result.append(x)
	return result

#def is_sub_graph(graph1, graph2):
#	len1 = len(graph1.nodes())
#	len2 = len(graph2.nodes())
#	set1 = set(graph1.nodes())
#	set2 = set(graph2.nodes())
#	min_graph = nx.Graph()
#	if len1 >= len2:
#		if set2.issubset(set1) == True:
#			min_graph = graph2
#		return (set2.issubset(set1), min_graph)
#	else:
#		if set1.issubset(set2) == True:
#			min_graph = graph1
#		return (set1.issubset(set2), min_graph)

def merge_communities(community1, community2):
	community_new = nx.Graph(C.subgraph((community1.nodes() + community2.nodes())))
	return community_new

#
#def get_all_cod(communities):
#	#计算社区中两两社区的社区重叠度,,未用
#	cod = []
#	for x in communities:
#		def inner_get_cod(y):
#			return get_communities_overlapping_degree(x, y)
#		cod.append(map(inner_get_cod, communities))
#	return cod

def merge_all_communities(communities):
	le = len(communities)
	communities_iter = communities
	bm = [0]*le
	result = []
	for i in range(le):
		for j in range(i+1, le):
			x = communities[i]
			y = communities[j]

			cod = get_communities_overlapping_degree(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 cod > gama:
				if is_subset(y,x):
					bm[j]=1
					break
				elif is_subset(x,y):
					bm[i] = 1
					break
				else: #other
					merge_graph = merge_communities(x, y)
					bm[i] = 1
					bm[j] = 1
					result.append(merge_graph)
					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 merge_all_communities(communities)
	else:
		return communities

def deal_seeds_GCE_inSDD(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 main():
	import sys
	global alpha,beta,gama,avg_type

	if len(sys.argv) > 2:
		avg_type=int(sys.argv[1])

	# read network dataset as graph C
	#nx.draw(C)
	#plt.savefig("karate_club_graph.png")
	# init
	cliques = get_maximum_cliques(C)
	communities = get_all_nature_community(cliques)


	# 合并
	results = merge_all_communities(communities)

	print "----------------------------------The detection result is: \n"
	print "all communities: "
	i = 1
	for x in communities:
		print "i = ", i, ":" , sorted(x.nodes())
		i += 1
	overlapping_nodes = set([])

	communities = [set(x) for x in communities]
	for x in communities:
		for y in communities[communities.index(x)+1:]:
			temp = x.intersection(y)
			overlapping_nodes = overlapping_nodes.union(temp)
	print "overlapping nodes are: ", sorted(list(overlapping_nodes))

#	for x in results:
#		nx.draw(x, node_color = (random(), random(), random()))
#	#nx.draw(nx.Graph(C.subgraph(overlapping_nodes)), node_color = "r")
#	plt.savefig("CD_output.png")

#if __name__ == '__main__':
#	import time
#	start = time.time()
#	#	import profile
#	#	profile.run("main()",sort=1)
#	main()
#	print "total time:", time.time() - start
#	#
#	#import pstats
#	#p = pstats.Stats('./pro_out')
#	#p.sort_stats("time").print_stats()