def newSLPAwauto(G, T=100):
egoLabelsNum = {}
egoLabelsNum = egoLabelsNum.fromkeys(G.nodes(), 1)
for ego in G.nodes():
ego_minus_ego = nx.ego_graph(G, ego, 1, False)
'利用非重叠的BGLL'
partition = BGLL.best_partition(ego_minus_ego)
'将partion换成community'
community_to_nodes = defaultdict(list)
for node, comID in partition.items():
community_to_nodes[comID].append(node)
'将ego加入每个社团'
for com in community_to_nodes.values():
com.append(ego)
'记录ego的标签个数'
egoLabelsNum[ego] = len(community_to_nodes)
'再用SLPAwAuto'
communities = SLPAwAuto(G, T, egoLabelsNum)
return communities
def runOrderedNodePreception(G,simThreshold,slpar=0.3):
'''
有访问顺序
:param G: 输入的图
:param simThreshold: 构建新的图的相似度
:return: 二维数组的社团列表结构
'''
'1.初始化社团标号为自己,注意一个点可以属于多个社团'
for n in G.nodes():
G.node[n]['communities']=[n]
'2.给节点排序'
#按节点的度降序排序
# visitedSequece=sorted(G.nodes(),key=lambda n:G.degree(n),reverse=True)
#按节点核值排序
# coreDict = nx.core_number(G)
# print coreDict
# visitedSequece = [t[0] for t in sorted(coreDict.items(),key=lambda d:d[1],reverse=True)]
# print visitedSequece
visitedSequece=computeCentrilityandSort4(G)
#存储节点是否该访问的标志
visiteFlag={}
for n in G.nodes():
visiteFlag[n]=False #所有节点初始化
'初始化'
allSubCommunities=[] ##所有的子社团
H=nx.Graph() ##重新构建的子图
subcomid_to_subcom={} ##H中节点的映射到包含的suncommunity
# subcom_to_suncomid={}
'3.找邻居社团，构建超图构成的子图H'
for ego in visitedSequece:
if(visiteFlag[ego]==False):
ego_minus_ego=nx.ego_graph(G,ego,1,center=False) ##不包含ego节点，1跳邻居
'访问标志'
for n in ego_minus_ego:
visiteFlag[n]=True
visiteFlag[ego]=True
'重叠LPA'
# community_to_nodes=overlappingLPA(ego_minus_ego,ego)
'利用非重叠的BGLL'
partition=BGLL.best_partition(ego_minus_ego)
'将partion换成community'
community_to_nodes=defaultdict(list)
for node,comID in partition.items():
community_to_nodes[comID].append(node)
'将ego加入每个社团'
for com in community_to_nodes.values():
com.append(ego)
'相同的子社团只允许出现一次'
for subcom in community_to_nodes.values():
sortedCom=tuple(sorted(subcom)) ##先给节点排个序
if not sortedCom in allSubCommunities:
'还没有该社团'
allSubCommunities.append(sortedCom) ##加入一个社团
id=len(allSubCommunities)-1 ##社团编号
# subcom_to_suncomid[sortedCom]=id ##subcom社团到id的映射
subcomid_to_subcom[id]=sortedCom ##H的id到suncom的映射
'创造H的节点'
Hnodes=H.nodes()
H.add_node(id)
'创造id与其他节点的边'
for n in Hnodes:
com=allSubCommunities[n]
sim=jaccardSim(sortedCom,com)
if sim>simThreshold:
'边不需要考虑方向'
H.add_edge(id,n,weight=sim)
else:
'该社团已经有过了'
continue
else:
continue
'3:对超图H进行社团搜索'
hcommunities=SLPAw(H,100,slpar,True)
'4:后处理，将超图节点还原成原来节点'
allCommunities=[]
for hcom in hcommunities.values():
com=set()
for hnode in hcom:
subcom=subcomid_to_subcom[hnode]
for i in subcom:
com.add(i)
sortedtmp=sorted(list(com))
allCommunities.append(sortedtmp)
# return allCommunities
'删掉嵌套的社团'
# # Remove nested communities
# nestedCommunities = set()
# N=len(allCommunities)
# for i in range(0,N):
# comm0=set(allCommunities[i])
# for j in range(i+1,N):
# comm1=set(allCommunities[j])
# print comm1
# if comm0.issubset(comm1):
# nestedCommunities.add(comm0)
# elif comm1.issubset(comm0):
# nestedCommunities.add(comm1)
#
# for comm in nestedCommunities:
# allCommunities.remove(list(comm))
return allCommunities
@file:test3.py
@time:2017/3/2313:15
"""
import communityLouvain as cd
import networkx as nx
import matplotlib.pyplot as plt
import random
from collections import defaultdict
from overlapping_modularity import compute_overlap_modularity_cxm
#better with karate_graph() as defined in networkx example.
#erdos renyi don't have true community structure
G = nx.karate_club_graph()
#first compute the best partitio
partition = cd.best_partition(G)
#drawing
size = float(len(set(partition.values())))
pos = nx.spring_layout(G)
count = 0.
for com in set(partition.values()):
count = count + 1.
list_nodes = [
nodes for nodes in partition.keys() if partition[nodes] == com
]
c = [random.random()] * len(list_nodes) # random color...
nx.draw_networkx_nodes(G,
pos,
list_nodes,
alpha=0.5,
def runNodePreception(G, simThreshold, slpar=0.3):
'''
:param G: 输入的图
:param simThreshold: 构建新的图的相似度
:return: 二维数组的社团列表结构
'''
'1.初始化社团标号为自己,注意一个点可以属于多个社团'
for n in G.nodes():
G.node[n]['communities'] = [n]
'2.找邻居社团，构建超图构成的子图H'
allSubCommunities = [] ##所有的子社团
H = nx.Graph() ##重新构建的子图
subcomid_to_subcom = {} ##H中节点的映射到包含的suncommunity
# subcom_to_suncomid={}
for ego in G.nodes():
ego_minus_ego = nx.ego_graph(G, ego, 1, center=False) ##不包含ego节点，1跳邻居
'利用非重叠的BGLL'
# community_to_nodes=overlappingLPA(ego_minus_ego,ego)
partition = BGLL.best_partition(ego_minus_ego)
'将partion换成community'
community_to_nodes = defaultdict(list)
for node, comID in partition.items():
community_to_nodes[comID].append(node)
'将ego加入每个社团'
for com in community_to_nodes.values():
com.append(ego)
'相同的子社团只允许出现一次'
for subcom in community_to_nodes.values():
sortedCom = tuple(sorted(subcom)) ##先给节点排个序
if not sortedCom in allSubCommunities:
'还没有该社团'
allSubCommunities.append(sortedCom) ##加入一个社团
id = len(allSubCommunities) - 1 ##社团编号
# subcom_to_suncomid[sortedCom]=id ##subcom社团到id的映射
subcomid_to_subcom[id] = sortedCom ##H的id到suncom的映射
'创造H的节点'
Hnodes = H.nodes()
H.add_node(id)
'创造id与其他节点的边'
for n in Hnodes:
com = allSubCommunities[n]
sim = jaccardSim(sortedCom, com)
if sim > simThreshold:
'边不需要考虑方向'
H.add_edge(id, n, weight=sim)
else:
'该社团已经有过了'
continue
'3:对超图H进行社团搜索'
hcommunities = SLPAw(H, 100, slpar, False)
'4:后处理，将超图节点还原成原来节点'
allCommunities = []
for hcom in hcommunities.values():
com = set()
for hnode in hcom:
subcom = subcomid_to_subcom[hnode]
for i in subcom:
com.add(i)
sortedtmp = sorted(list(com))
allCommunities.append(sortedtmp)
return allCommunities