def showdegree(flag):
namemat = []
Degreedis = []
namemat = webmodel.readArff('E:/write.xls', flag)
Num = namemat.__len__() # 节点个数
for i in range(0, 63):
Degreedis.append(0)
i = 0
for line in namemat:
Degreedis[i] = sum(line)
i += 1
# print Degreedis
temp = []
for j in range(0, 63):
temp.append(j)
fig1 = plt.figure(2)
rects = plt.bar(left=(temp[0:63]),
height=(Degreedis[0:63]),
width=1,
align="center",
yerr=0.000001)
plt.title('The degree of these nodes')
plt.xlabel('Number of nodes')
plt.ylabel('Degree')
plt.show()
def getclustering_co(flag):
namemat = []
EEE = []
CCC = []
namemat = webmodel.readArff('E:/write.xls', flag)
Num = namemat.__len__() # 节点个数
# print namemat
#print Num
for i in range(0, 63):
EEE.append(0)
CCC.append(0)
for i in range(0, 63):
neighbornum = 0
neighbor = []
for j in range(0, 63):
if i != j and namemat[i][j] == 1:
neighbornum += 1
neighbor.append(j)
for linea in neighbor:
for lineb in neighbor:
if namemat[linea][lineb] == 1:
EEE[i] += 1
EEE[i] = EEE[i] / 2
# print EEE[i]
if (EEE[i] == 0):
CCC[i] = 0
else:
CCC[i] = 2 * EEE[i] / 1.0 / (neighbornum * (neighbornum - 1))
aver = sum(CCC) / 1.0 / Num
print aver
def corenessca(flagg):
namemat = []
coreness = []
Degreedis = []
namemat = webmodel.readArff('E:/write.xls', flagg)
Num = namemat.__len__() # 节点个数
print namemat
for i in range(0, Num):
coreness.append(0)
Degreedis.append(0)
flag = 0
while flag < 63:
v = 0
for line in namemat:
Degreedis[v] = sum(line)
v += 1
for j in range(0, Num):
if Degreedis[j] == flag:
print('Number %d node has coreness: ' % (j + 1), flag)
#print flag
for k in range(0, Num):
namemat[k][j] = 0
namemat[j][k] = 0
v = 0
for line in namemat:
Degreedis[v] = sum(line)
v += 1
flag2 = 0
for l in range(0, Num):
if Degreedis[l] == flag and flag != 0:
flag2 = 1
if flag2 == 0:
flag += 1
def calculationtwo(flag):
lenthma = []
lenthma = webmodel.readArff('E:/write.xls',flag)
Num = lenthma.__len__() # 节点个数
lenthmat=copy.deepcopy(lenthma)
for m in range(0,Num):
for n in range(0, Num):
if lenthmat[m][n]==0:
lenthmat[m][n]='INF'
for m in range(0, Num):
lenthmat[m][m]=0
for k in range(0,Num):
for i in range(0, Num):
for j in range(0, Num):
if lenthmat[i][k] != 'INF' and lenthmat[k][j] != 'INF':
if lenthmat[i][j] > lenthmat[i][k] + lenthmat[k][j]:
lenthmat[i][j] = lenthmat[i][k] + lenthmat[k][j]
sumlen=0
for i in range(0,Num):
for j in range(0,Num):
if lenthmat[i][j]!='INF':
sumlen = sumlen + lenthmat[i][j]
ave=sumlen/1.0/(Num*(Num-1))
print("aver_path lengh:", ave)
return ave
def drawnetgraph(Num):
lenthmat = webmodel.readArff('E:/write.xls', Num)
G = nx.Graph()
Matrix = np.array([])
Matrix = np.array(lenthmat)
#print len(Matrix)
for i in range(0, 63):
for j in range(0, 63):
if Matrix[i][j] == 1:
G.add_edge(i, j)
del lenthmat
nx.draw(G, node_size=20)
plt.show()
def showdistribution(flag):
namemat = []
Degreedis = []
namemat = webmodel.readArff('E:/write.xls', flag)
Num = namemat.__len__() # 节点个数
for i in range(0, 63):
Degreedis.append(0)
i = 0
for line in namemat:
Degreedis[i] = sum(line)
i += 1
# print Degreedis
temp = []
for j in range(0, 63):
temp.append(j)
aver_degree = sum(Degreedis) / Num
M = max(Degreedis) # 最大的度
print M
Degreedistribution = []
dnum = []
for m in range(0, M + 1):
Degreedistribution.append(0)
dnum.append(0)
for item in Degreedis:
for n in range(0, M + 1):
if item == n:
dnum[n] += 1
for k in range(0, M + 1):
Degreedistribution[k] = dnum[k] / 1.0 / Num
fig1 = plt.figure(2)
rects = plt.bar(left=(temp[0:M + 1]),
height=(Degreedistribution[0:M + 1]),
width=1,
align="center",
yerr=0.000001)
plt.title('The degreedistribution of these nodes')
plt.xlabel('Number of nodes')
plt.ylabel('Degreedistribution')
plt.show()
def largestsubgraph(flagg):
namemat = []
namemat = webmodel.readArff('E:/write.xls', flagg)
Num = namemat.__len__() # 节点个数
scatterpr = []
scatterpi = []
Degreedis = []
tempa = []
tempb = []
largesta = []
largestb = []
for i in range(0, 64):
largesta.append(0)
largestb.append(0)
aa = copy.deepcopy(namemat)
bb = copy.deepcopy(namemat)
Num = namemat.__len__()
for line in namemat:
Degreedis.append(sum(line))
#print Degreedis
# intentional_attack后
tempa = getfloyd(aa)
print tempa
k = 0
for line in tempa:
for j in line:
if j != 0 and j != 'INF':
largesta[k] += 1
k += 1
terma = max(largesta)
scatterpi.append(terma)
print terma
for j in range(0, Num):
k = 0
for i in range(0, 63):
largesta[i] = 0
maxnode = Degreedis.index(max(Degreedis))
Degreedis[maxnode] = 0
for m in range(0, Num):
aa[m][maxnode] = 0
aa[maxnode][m] = 0
tempa = getfloyd(aa)
for line in tempa:
for j in line:
if j != 0 and j != 'INF':
largesta[k] += 1
k += 1
terma = max(largesta)
scatterpi.append(terma)
# random_attack后
tempb = getfloyd(bb)
k = 0
for line in tempb:
for j in line:
if j != 0 and j != 'INF':
largestb[k] += 1
k += 1
termb = max(largestb)
scatterpr.append(termb)
b_list = range(0, 63)
blist_webId = random.sample(b_list, 63)
for j in range(0, Num):
k = 0
for i in range(0, 63):
largestb[i] = 0
temp = blist_webId[j]
for m in range(0, Num):
bb[m][temp] = 0
bb[temp][m] = 0
tempb = getfloyd(bb)
for line in tempb:
for j in line:
if j != 0 and j != 'INF':
largestb[k] += 1
k += 1
termb = max(largestb)
scatterpr.append(termb)
print scatterpi
print scatterpr
x = np.arange(0, 64)
y = x
fig = plt.figure()
ax1 = fig.add_subplot(111)
# 设置标题
ax1.set_title('Attack!!!')
# 设置X轴标签
plt.xlabel('Number')
# 设置Y轴标签
plt.ylabel('Maximum Connected Subgraph')
# 画散点图
ax1.scatter(x, scatterpi, s=12, c='r', marker='o')
ax1.scatter(x, scatterpr, s=10, c='b', marker='o')
# 设置图标
label = ["intentional", "random"]
plt.legend(label, loc=0, ncol=2)
# 显示所画的图
plt.show()
def aver_pathle(flagg):
namemat = []
namemat = webmodel.readArff('E:/write.xls', flagg)
Num = namemat.__len__() # 节点个数
scatterpr = []
scatterpi = []
Degreedis = []
Num = namemat.__len__()
aa = copy.deepcopy(namemat)
bb = copy.deepcopy(namemat)
for line in namemat:
Degreedis.append(sum(line))
# intentional_attack后
ava = aver_shpathl.calculation(bb, Num)
scatterpi.append(ava)
jin = Num
for j in range(0, Num):
maxnode = Degreedis.index(max(Degreedis))
Degreedis[maxnode] = 0
for k in range(0, Num):
aa[k][maxnode] = 0
aa[maxnode][k] = 0
jin -= 1
if jin == 0 or jin == 1:
scatterpi.append(0)
else:
ave = aver_shpathl.calculation(bb, jin)
scatterpi.append(ave)
# random_attack后
b_list = range(0, 63)
blist_webId = random.sample(b_list, 63)
ave = aver_shpathl.calculation(bb, Num)
scatterpr.append(ave)
jim = Num
for j in range(0, Num):
temp = blist_webId[j]
for k in range(0, Num):
bb[k][temp] = 0
bb[temp][k] = 0
jim -= 1
if jim == 0 or jim == 1:
scatterpr.append(0)
else:
ave = aver_shpathl.calculation(bb, jim)
scatterpr.append(ave)
x = np.arange(0, 64)
y = x
fig = plt.figure()
ax1 = fig.add_subplot(111)
# 设置标题
ax1.set_title('Attack!!!')
# 设置X轴标签
plt.xlabel('Number')
# 设置Y轴标签
plt.ylabel('Average Path Length')
# 画散点图
ax1.scatter(x, scatterpi, s=12, c='r', marker='o')
ax1.scatter(x, scatterpr, s=10, c='b', marker='o')
# 设置图标
label = ["intentional", "random"]
plt.legend(label, loc=0, ncol=2)
# 显示所画的图
plt.show()
lenthmat[m][m]=0
for k in range(0,Num):
for i in range(0, Num):
for j in range(0, Num):
if lenthmat[i][k] != 'INF' and lenthmat[k][j] != 'INF':
if lenthmat[i][j] > lenthmat[i][k] + lenthmat[k][j]:
lenthmat[i][j] = lenthmat[i][k] + lenthmat[k][j]
sumlen=0
for i in range(0,Num):
for j in range(0,Num):
if lenthmat[i][j]!='INF':
sumlen = sumlen + lenthmat[i][j]
ave=sumlen/1.0/(Num*(Num-1))
return ave
if __name__ == '__main__':
lenthmat = []
lenthmat = webmodel.readArff('E:/write.xls',0)
Num = lenthmat.__len__()#节点个数
#print namemat
print Num
#a = [[0, 1, 1, 0, 1], [1, 0, 1, 1, 0], [1, 1, 0, 0, 0], [0, 1, 0, 0, 0], [1, 0, 0, 0, 0]]
# n=a.__len__()
ave=calculation(lenthmat, Num)
print("aver_path lengh:", ave)