论文复现1--The architecture of complex network

import networkx as nximport matplotlib.pyplot as plt#import pandas as pd#from networkx.algorithms import approximation as approx#复现论文Alain Barrat.Modeling the evolutio of Weighted networks#yyh#2018/3/24pathOfFile1 = "D:\soft\cond-mat_gml.adj";pathOfFile2 = "D:\soft\cond-mat-2005_gml.adj";title1 = "D:\cond-mat_gml";title2 = "cond-mat-2005_gml";#读入数据,返回无向加权graphdef createGraph(filename) : ???G = nx.Graph() ???for line in open(filename) : ???????strlist = line.split() ???????n1 = int(strlist[0]) ???????n2 = int(strlist[1]) ???????weight = float(strlist[2]) ???????G.add_weighted_edges_from([(n1, n2, weight)]) ???return G#test ????GG = createGraph(pathOfFile1);#得到每一个顶点的strengthdef getStrengthList(G): ???nodelist = G.nodes(); ???strengthlist =[]; ???for i in nodelist: ??????strengthlist.append(G.degree(i,weight=‘weight‘)); ???return strengthlist#得到强度的字典#返回字典def getStrengthDic(G): ???strengthDic = {}; ???strengthlist = getStrengthList(G); ???nodelist = G.nodes(); ???count = 0; ???for i in nodelist: ???????strengthDic[i] = strengthlist[count]; ???????count = count+1; ???return strengthDic;# strengthDic = getStrengthDic(G);#得到每一个顶点的degree#返回字典def getDegreeDic(G): ??return G.degree();#返回每一个顶点的度#返回列表def getDegreeList(G): ???nodelist = G.nodes(); ???degreelist = []; ???degreeDic = getDegreeDic(G); ???for i in nodelist: ???????degreelist.append(degreeDic[i]); ???return degreelist;#得到度的最大值和最小值，并且返回其值def getMaxMinDegree(degreeDic): ???maxDegree=0; ???minDegree = 0; ???????for k,v in degreeDic: ???????if v>maxDegree: ???????????maxDegree = v; ???????if v<minDegree : ???????????minDegree = v; ???????return (maxDegree,minDegree);#得到每个顶点的聚集系数#根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式与networkx中API 数学公式一致#返回字典def getCiDic(G): ???clusteringdic = nx.clustering(G,nodes=None,weight=None); ???return clusteringdic; ???????#得到每个顶点的加权聚集系数 #根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式CiW = 1/si/(ki-1)*sum((wij+wih)/2|i,j,h,互连)#返回字典def getCiWDic(G): ???strengthlist = getStrengthList(G); ???degreedic = G.degree(); ???????nodelist = G.nodes(); ???cw = {}; ???sum = 0; ???count = 0; ???for i in nodelist: ???????????????si = strengthlist[count]; ???????count = count+1; ???????neibors= G.neighbors(i); ???????ki = degreedic[i]; ???????if ki>1: ?#最起码构成三角形，若不能则默认为0 ?????????sum = 0; ?????????for j in ?neibors: ?????????????for h in G.neighbors(j): ????????????????if G.get_edge_data(i,h)!=None: ???????????????????wij = G.get_edge_data(i,j)[‘weight‘]; ???????????????????wih = G.get_edge_data(i,h)[‘weight‘]; ???????????????????sum +=(wij+wih)/2;# 注意在这个地方要除以4 因为多计算了一遍 ????????????????????????????????????????????ciw = 1/(si*(ki-1))*sum; ?????????cw[i] = ciw; ???????else: ?????????cw[i]=0; ???return cw ;#test ???cw = getCWDic(G);#得到每个顶点的近邻度#根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式Knni = 1/Ki*sum(kj|j,i相连)#返回字典def getKnnDic(G): ???degreeDic = ?getDegreeDic(G); ???knnDic = {}; ???nodes = G.nodes(); ???for i in nodes: ???????????????ki = degreeDic[i]; ???????????????if ki>0: #当该顶点没有近邻点是默认为0 ???????????neighborOfi = G.neighbors(i); ???????????sum = 0; ???????????for j in neighborOfi: ???????????????kj = degreeDic[j]; ???????????????sum += kj; ???????????knni = 1/ki*sum; ???????else: ???????????knni = 0; ???????knnDic[i] = knni; ???return knnDic;#knnDic = getKnnDic(G)#得到每个顶点的加权近邻度#根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式knnwi = 1/si*sum(wij*kj)def getKnnWDic(G): ???strengthDic = getStrengthDic(G); ???degreeDic = getDegreeDic(G); ???nodes = G.nodes(); ???knnw = {}; ???for i in nodes: ???????sum = 0; ???????si = strengthDic[i]; ???????if si>0: ?#若是孤立的顶点则为0 ????????????for j in G.neighbors(i): ???????????????wij = G.get_edge_data(i,j)[‘weight‘]; ??#得到ji 之间的权值 ???????????????kj = degreeDic[j]; ???????????????sum += wij*kj; ???????????knnwi = 1/si*sum; ???????else: ???????????knnwi = 0; ???????knnw[i] = knnwi; ???return knnw;#返回一个节点的所有信息#包括节点编号，顶点的度，顶点的强度，顶点的聚集系数，#顶点的加权聚集系数，顶点的近邻度#顶点的加权近邻度def getMessaures(G): ???return 0;#全概率公式 #根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式C(k) = sum(Ki*p(ki/k))#每一个度对应的KNNWOFK#返回字典def getKnnOfKDic(G): ???knnDic = getKnnDic(G); ???degreeDic = getDegreeDic(G); ???return getAvarageOfKDic(knnDic,degreeDic);#每一个度的对应的KNNW#根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#返回字典 ???def getKnnWOfKDic(G): ???knnWDic = getKnnWDic(G); ???degreeDic = getDegreeDic(G); ???return getAvarageOfKDic(knnWDic,degreeDic); ???#全概率公式#根据论文 Alain Barrat.Modeling the evolutio of Weighted networks中#介绍的公式C(K) = 1/NP(K)*sum(Ci|i/ki=k)#每一个度对应的CiK#返回字典def getCiOfkDic(G): ???ciDic = getCiDic(G); ???degreeDic = getDegreeDic(G); ???return getAvarageOfKDic(ciDic,degreeDic);#利用概率公式#每个度对应的CIW#返回字典def getCiWOfKDic(G): ???ciWDic = getCiWDic(G); ???degreeDic = getDegreeDic(G); ???return getAvarageOfKDic(ciWDic,degreeDic); ???#利用概率公式#每个度对应的平均strength#返回字典def getStrengthOfKDic(G): ???strengthDic = getStrengthDic(G); ???degreeDic= getDegreeDic(G); ???return ?getAvarageOfKDic(strengthDic,degreeDic);#统计strength 频数 ??按照区间来进行统计#strength可能是一个浮点数#按照def getStrengthcountList(G): ???strengthlist =getStrengthList(G); ???#频度统计，统计max-min，将其化成整数值，依照区间来进行频数统计 ???strengthlist.sort(reverse = False) ???maxstrength = max(strengthlist); ???minstrength = min(strengthlist); ???splitnum = int(maxstrength- minstrength);#区间数目 ???strengthcount = [0 for i in range(splitnum)]; ???spli = float(maxstrength-minstrength)/(splitnum-1); #注意此处splitnum-1 ???for i in strengthlist: ??????index = (int((i-minstrength)/spli)); ??????#print(index) ??????strengthcount[index] = strengthcount[index]+1 ; ?????return strengthcount; ??????#绘制双对数分布图形def ploDistrbution(countList,xlabel,ylabel,title): ????#plt.figure(figsize=(500, 500)); ???????x = range(len(countList)) ????y = [z / float(sum(countList)) for z in countList] ??????plt.xlabel(xlabel); ?????plt.ylabel(ylabel); ????plt.title(title); ????plt.loglog(x,y,color="blue",linewidth=1,marker = ‘s‘); ????plt.show()#显示图表 ??????return ‘ok‘ ????#根据字典关键字来升序排序,采用选择排序法 ???#返回字典def sortDic(dic): ???????return sorted(dic.iteritems(),key=lambda abs:abs[0],reverse=False) ??#合并两个列表，返回字典def ?mergeList(list1,list2): ???dic = {}; ???return dic; ???#合并两个字典，返回字典,对应合并def mergeDic(dic1,dic2): ???dic = {}; ???for key,value in dic1: ????????dic[value] = dic2[value]; ???return dic;#得到字典的keys列表#得到字典的values列表#返回其列表元组def getKeysValuesListOfDic(dic): ??keys = dic.keys(); ??values = dic.values(); ??return (keys,values);#得到度量在K条件下的平均值#返回字典（关于K条件下的平均值序列）def getAvarageOfKDic(metricDic,degreeDic): ???#得到度的范围 ???(maxDegree,minDegree) = getMaxMinDegree(degreeDic); ???avarageMetricsOfKDic = {}; #初始化 ???value = 0; ?#求和值 ???count=0; #计数度的个数 ???for k in range(maxDegree): ???????value = 0; ???????count = 0; ???????for nodes,degree in degreeDic: ???????????if degree==k: ???????????????count += 1; ???????????????value +=metricDic[nodes]; ???????if count>=1: ???????????value =float(value/count); ???????else : ???????????value = 0; ???????????????avarageMetricsOfKDic[k] = value; ???????????return avarageMetricsOfKDic; #绘制S与K的关系图def plotStrengthAndDegree(G,kdic,xlabel,ylabel,title): ???strengthOfKDic = getStrengthOfKDic(G); ???strengthOfKDic = dataBanning( strengthOfKDic ,kdic); ???(x,y) = getKeysValuesListOfDic( strengthOfKDic); ??????????plt.xlabel(xlabel); ???plt.ylabel(ylabel); ???plt.title(title); ???plt.loglog(x,y,color="red",marker = ‘o‘); ????????plt.show(); ???????return ‘ok‘ ???#data Banning#输入列表或者是字典#返回字典def ?dataBanning(dicOfK,degreeDic): ????sum =0.0; ????count = 0; ????dic = {}; ????(maxDegree,minDDegree) = getMaxMinDegree(degreeDic); ????#print(maxDegree/10); ?????????for i in range(1,10): ????????dic[i] = dicOfK[i]; ??????????????????????for i in range(1,int(maxDegree/10)): ???????sum=0; ???????count =0; ???????for j in range(0,9): ???????????if ?(i*10+j)<= maxDegree and dicOfK[i*10+j]!=0: ???????????????sum += dicOfK[i*10+j]; ???????????????count += 1; ???????????else: ???????????????break; ???????if count!=0: ??????????sum = sum/count; ???????else: ??????????continue; ???????dic[10*i+6] = sum; ????????????return dic; ?#绘制Ck,CWk ?#绘制KNNK，KNNWKdef plotCompare(mic1,mic2,kdic,xlabel,ylabel,title): ????????mic1Dic = dataBanning(mic1,kdic); ????(x,y1) = getKeysValuesListOfDic(mic1Dic); ????micW2Dic = dataBanning( mic2 ,kdic); ????(x,y2) = getKeysValuesListOfDic(micW2Dic) ????????????plt.xlabel(xlabel); ????plt.ylabel(ylabel); ????plt.title(title); ????plt.loglog(x, y1, ‘go‘, x, y2, ‘ro‘); ????plt.show(); ????return ‘OK‘;def test(G,title): ???#strengthcount = getStrengthcountList(G); ???#ploDistrbution(strengthcount,"s(number of papers)","p(s)",title) ???#plt.savefig("p(s).jpg") ; ???????????????????#degreecount = ?nx.degree_histogram(G) ???#ploDistrbution(degreecount,"k(number of collaborators)","p(k)",title) ???#plt.savefig(("p(k)"+title).jpg) ; ???????????degreeDic = getDegreeDic(G); ???#strengthlist ?= getStrengthDic(G); ???xlabel = ‘K(number of collaborators)‘; ???ylabel = ‘S(number of paper)‘; ???plotStrengthAndDegree(G,degreeDic,xlabel,ylabel,title) ???????????#绘制Ck,CWk ???ciOfKDic = getCiOfkDic(G); ???ciWOfKDic = getCiWOfKDic(G); ???plotCompare(ciOfKDic,ciWOfKDic, degreeDic,"K(number of collaborators)","C(K),CW(K)",title); ?????#绘制Knnk,Knnwk ???knnOfKDic = getKnnOfKDic(G); ???knnWOfKDic = getKnnWOfKDic(G); ???plotCompare(knnOfKDic,knnWOfKDic, degreeDic,"K(number of collaborators)","Knn(k),KnnW(K)",title); ???????????????????return ‘ok‘;#test1#G1 = createGraph(pathOfFile1);#test(G1,title1);#test2G2 = createGraph(pathOfFile2);test(G2,title2)#绘制图形#clusteringL = nx.clustering(G,nodes=None,weight=None)#clusteringDic = nx.clustering(G,nodes=None,weight=None);#G.get_edge_data(1,190)#查看固定的边#n= len(G) #查看节点数#聚集系数#Clustering = nx.clustering(G)#网络度分布#Degree_distribution = nx.degree_histogram(G)#网络度的中心性#Degree_Centrality = nx.degree_centrality(G)#各个节点Closeness#Closeness_Centrality = nx.closeness_centrality(G)# #各个节点Betweenness# #Betweenness_Centrality = nx.betweenness_centrality(G)#nx.betweenness_centrality(G)????