m=G.size()

nm=newG.size()

q=nq=0

for coms in Bestcomps:

subG=G.subgraph(coms)

l=subG.size()

list_d=[x for y,x in G.degree_iter(coms)]

d=0

for k in list_d:

d=d+k

q=q+float(l)/m+(float(d)/(2*m))**2

subN=newG.subgraph(coms)

nl=subN.size()

list_nd=[a for b,a in newG.degree_iter(coms)]

nd=0

for k in list_nd:

nd=nd+k

nq=nq+float(nl)/nm+(float(nd)/(2*nm))**2

ng=g.copy()

ng.remove_edges_from(ng.edges())

for i in range(len(cl)-1):#连接簇之间不变的线

j=i+1

while j<len(cl):

for x in itertools.product(cl[i],cl[j]):#簇之间两两（cl[i],cl[j]）笛卡尔积

if g.has_edge(x[0],x[1]):

ng.add_edge(x[0],x[1])

j+=1

sub=[]

for i in range(len(cl)):#打乱簇内线

sub=g.subgraph(cl[i])

edges=sub.edges()

numOfe=sub.number_of_edges()

sub.remove_edges_from(edges)

setE=[]

tupleE=[]

for k in range(numOfe):#生成numOfe条线

l=set(random.sample(cl[i],2))#随机生成cl[i]内两个数，并生成集合，因为集合无序，容易判断该两个数是否已经生成了

while l in setE:

l=set(random.sample(cl[i],2))

setE.append(l)

for item in setE:#集合变元组，用来添加边

tupleE.append(tuple(item))

ng.add_edges_from(tupleE)

j={}

k=0

for best in Bestcomps:

best_val=0

for c in newComps:

val=len([val for val in best if val in c])

if val>best_val:

best_val=val

j[k]=float(best_val)/float(len(best))

k+=1

G.clear()

import shlex

try:

f=open(path,'r')

except :

print "readfile_net error"

for line in f:

if line.lower().startswith("*edges"):

break

for line in f:

temp=line.split()

if len(temp)<2:

continue

u,v=temp[0:2]

if u!=v:#去除单节点

G.add_edge(u,v)

n = G.number_of_nodes() #|V|

A = nx.adj_matrix(G) #adjacenct matrix

m_ = 0.0 #the weighted version for number of edges

for i in range(0,n):

for j in range(0,n):

m_ += A[i,j]

m_ = m_/2.0

#calculate the weighted degree for each node

Orig_deg = {}

Orig_deg = UpdateDeg(A, G.nodes())

#run Newman alg

Bestcomps=runGirvanNewman(G, Orig_deg, m_)

deglist=list()

for k,v in nx.degree(G).items():

deglist.append({'name':k,'deg':v})

n = G.number_of_nodes()

neigbu=G[u]

setu=set(neigbu.keys())

neigbv=G[v]

setv=set(neigbv.keys())

if u in setv:

setv.remove(u)

setu.remove(v)

val=setu^setv

p=len(val)

m=0

for i in list:

m=node_dis(G,v,i)+m

n=len(list)

temp=[]

for each in deglist:

if v!=each['name']:

temp.append(each)

deglist=temp

for i in range(len(delist)):

if (u in delist[i] and v in delist[i]):

return True

#i=1#记录分组数目

#j=1#记录候选列表中节点数目

#q=1#记录候选分组中的分组数目

global dist

M=[]#候选节点列表

cl=[]

m=(len(deglist))/int(k)

#while(deglist!= None):

while (len(deglist)>k):

for i in range(m):

seed=deglist[0]['name']

#index(seed,Bestcomps)

cl.append([seed])

#del deglist[0]

deglist=delnode(G,seed,deglist)

while (len(cl[i])<k):

M=[]#sam

v=deglist[0]['name']

mindist=dist(G,v,cl[i])

for node in deglist:#找出最小距离的节点

j=node['name']

dis=dist(G,j,cl[i])

if dis<mindist:

mindist=dis

bestnode=j

for node in deglist:#找出最小距离的节点集合

j=node['name']

dis=dist(G,j,cl[i])

if dis==mindist:

M.append(j)

if len(M)>1:#候选节点有多个的情况

j=0

while (j<len(M)):

#print seed,M[j],Bestcomps,match(G,seed,M[j],Bestcomps)

if(match(G,seed,M[j],Bestcomps)):

cl[i].append(M[j])

deglist=delnode(G,M[j],deglist)

break

else:

j=j+1

if (j==len(M)-1):

cl[i].append(M[j])

deglist=delnode(G,M[j],deglist)

break

# for v in M:

# if(match(seed,v,Bestcomps)):

# cl[i].append(v)

# delnode(v,deglist)

# else:

# cl[i].append(M[len(M)-1])

# delnode(M[len(M)-1],deglist)

if (len(M)==1):#候选节点只有一个

cl[i].append(M[0])

deglist=delnode(G,M[0],deglist)

#for remain in deglist:#处理剩下的少于k个的节点

while(len(deglist)!=0):

remain=deglist[0]

v=remain['name']

mincl=dist(G,v,cl[0])

bestcl=0

for i in range(len(cl)):

if(match(G,cl[i][0],v,Bestcomps)):#优先加入到与seed同社区的分组中

bestcl=i

break

#cl[i].append(j)

#delnode(j,deglist)

else:

distcl=dist(G,v,cl[i])

if distcl<mincl:

mincl=distcl

bestcl=i

cl[bestcl].append(v)

deglist=delnode(G,v,deglist)

n=G.number_of_nodes()

sum=0.0

for g in nx.connected_component_subgraphs(G):

m=g.number_of_nodes()

if m==1:

pass

else:

sum=sum+(1.0*m/n)*(nx.average_shortest_path_length(g))

#plt.subplot(111)

## plt.figure("Original Graph")

## nx.draw(G,with_labels = True,pos=nx.spring_layout(G))

## plt.show()

toG=G.copy()#togn会改变G，用副本传递

Bestcomps=togn(toG)#初探社区

deglist=graphtodegree(G)

deglist.sort(key=lambda deg:(-deg['deg'],deg['name']),reverse=False)#将节点降序排列

cl=k_cluster(G,deglist,k,Bestcomps)#分簇

nG=disturb(G,cl)#扰乱后新的图

filepath="./data/9.txt"

H=nx.Graph()# undirect

G=H.to_undirected()

readfile_net(G,path=filepath)

print nx.average_clustering(G)#CC

print APL(G)#APL

#togn(G)

toG=G.copy()#togn会改变G，用副本传递

Bestcomps=togn(toG)#初探社区

readfile_net(G,path=filepath)

deglist=graphtodegree(G)

deglist.sort(key=lambda deg:(-deg['deg'],deg['name']),reverse=False)#将节点降序排列

cl=k_cluster(G,deglist,3,Bestcomps)#分簇

#new G

newG=disturb(G,cl)

print nx.average_clustering(newG)#CC

print APL(newG)#APL

toN=newG.copy()

newComps=togn(toN)#新的划分

j=analyze(newComps,Bestcomps)

com=0.0

for i in range(len(j)):

com+=j[i]

average=com/len(j)

print "average lost：",average

delta_q=modularity(G,newG,Bestcomps)