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average_path.py
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average_path.py
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import networkx as nx
import random
import numpy as np
import matplotlib.pyplot as plt
def Judge_in_graph(G, node1, node2, node3, node4):
edges = list(G.edges())
tag = False
if (node1, node2) not in edges and (node3, node4) not in edges:
if (node2, node1) not in edges and (node4, node3) not in edges:
tag = True
return tag
def calculate_ass(G, p , Iteration):
i = 0
while i < Iteration:
#print(i)
edge = list(G.edges())
degree = nx.degree(G)
index1 = random.randint(0,len(G.edges)-1) ##随机获取第一条边
index2 = random.randint(0,len(G.edges)-1) ##随机获取第二条边
edge1 = edge[index1]
edge2 = edge[index2]
k1 = degree[edge1[0]]
k2 = degree[edge1[1]]
k3 = degree[edge2[0]]
k4 = degree[edge2[1]]
tag = 0
if edge1[0] != edge2[0] and edge1[0] != edge2[1] and edge1[1] != edge2[0] and edge1[1] != edge2[1]:
tag = 1
order_arr = [[edge1[0], edge1[1], edge2[0], edge2[1]],[k1, k2, k3, k4]]
order_arr = np.array(order_arr)
order_arr = order_arr[:, order_arr[1].argsort()]
p1 = random.random()
############在网络中移除原来的边并加上新的边##########
if p1 < p and tag == 1:
judge_tag = Judge_in_graph(G,order_arr[0][0], order_arr[0][1],order_arr[0][2], order_arr[0][3])
if judge_tag:
G.add_edge(order_arr[0][0], order_arr[0][1])
G.add_edge(order_arr[0][2], order_arr[0][3])
G.remove_edge(edge1[0], edge1[1])
G.remove_edge(edge2[0], edge2[1])
i = i + 1
#(i)
#print(i)
if p1 >= p and index1 != index2 and tag == 1:
#################随机重连####################
rand_index = random.sample(range(0, 4), 4)
judge_tag = Judge_in_graph(G, order_arr[0][rand_index[0]], order_arr[0][rand_index[1]], order_arr[0][rand_index[2]], order_arr[0][rand_index[3]])
if judge_tag:
G.remove_edge(edge1[0], edge1[1])
G.remove_edge(edge2[0], edge2[1])
G.add_edge(order_arr[0][rand_index[0]], order_arr[0][rand_index[1]])
G.add_edge(order_arr[0][rand_index[2]], order_arr[0][rand_index[3]])
i = i + 1
#r1 = round(nx.degree_assortativity_coefficient(G),6)
M = nx.degree_mixing_matrix(G)
M2 = M*M
r1 = (M.trace() - np.sum(M2)) / (1 - np.sum(M2))
conncected = nx.is_connected(G)
if conncected:
r2 = round(nx.average_shortest_path_length(G), 6)
else:
sv = 0
coun = 0
for C in (G.subgraph(c).copy() for c in nx.connected_components(G)):
v = nx.average_shortest_path_length(C)
sv = sv + v
coun = coun + 1
r2 = sv / coun
return r1, r2
def calculate_disass(G, p , Iteration):
i = 0
while i < Iteration:
print(i)
edge = list(G.edges())
degree = nx.degree(G)
index1 = random.randint(0,len(G.edges)-1) ##随机获取第一条边
index2 = random.randint(0,len(G.edges)-1) ##随机获取第二条边
edge1 = edge[index1]
edge2 = edge[index2]
k1 = degree[edge1[0]]
k2 = degree[edge1[1]]
k3 = degree[edge2[0]]
k4 = degree[edge2[1]]
tag = 0
if edge1[0] != edge2[0] and edge1[0] != edge2[1] and edge1[1] != edge2[0] and edge1[1] != edge2[1]:
tag = 1
order_arr = [[edge1[0], edge1[1], edge2[0], edge2[1]],[k1, k2, k3, k4]]
order_arr = np.array(order_arr)
order_arr = order_arr[:, order_arr[1].argsort()]
p1 = random.random()
############在网络中移除原来的边并加上新的边##########
if p1 < p and tag == 1:
judge_tag = Judge_in_graph(G,order_arr[0][0], order_arr[0][3],order_arr[0][1], order_arr[0][2])
if judge_tag:
G.add_edge(order_arr[0][0], order_arr[0][3])
G.add_edge(order_arr[0][1], order_arr[0][2])
G.remove_edge(edge1[0], edge1[1])
G.remove_edge(edge2[0], edge2[1])
i = i + 1
if p1 >= p and index1 != index2 and tag == 1:
#################随机重连####################
rand_index = random.sample(range(0, 4), 4)
judge_tag = Judge_in_graph(G, order_arr[0][rand_index[0]], order_arr[0][rand_index[1]], order_arr[0][rand_index[2]], order_arr[0][rand_index[3]])
if judge_tag:
G.remove_edge(edge1[0], edge1[1])
G.remove_edge(edge2[0], edge2[1])
G.add_edge(order_arr[0][rand_index[0]], order_arr[0][rand_index[1]])
G.add_edge(order_arr[0][rand_index[2]], order_arr[0][rand_index[3]])
i = i + 1
#r1 = round(nx.degree_assortativity_coefficient(G),6)
M = nx.degree_mixing_matrix(G)
M2 = M*M
r1 = (M.trace() - np.sum(M2)) / (1 - np.sum(M2))
conncected = nx.is_connected(G)
if conncected:
r2 = round(nx.average_shortest_path_length(G), 6)
else:
sv = 0
coun = 0
for C in (G.subgraph(c).copy() for c in nx.connected_components(G)):
v = nx.average_shortest_path_length(C)
sv = sv + v
coun = coun + 1
r2 = sv / coun
return r1, r2
Iteration = 10000 ##迭代次数
P = [1, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0]
assortivity = np.zeros((21,1))
average_path = np.zeros((21,1))
count = 0
for i in P:
print(i)
for j in range(10):
G = nx.read_edgelist('datasets/network_average_path.txt', nodetype=int)
r1, r2 = calculate_ass(G, i, Iteration)
print(r1, r2)
assortivity[count] = assortivity[count] + r1
average_path[count] = average_path[count] + r2
assortivity[count] = assortivity[count] / 10
average_path[count] = average_path[count] / 10
count = count + 1
fid = open('results/average_path_ass.txt', 'w')
for i in range(len(P)):
fid.write(str(assortivity[i])+' '+str(average_path[i])+'\n')
fid.close()
plt.figure(figsize=(16,16))
plt.style.use('ggplot')
plt.semilogx(assortivity,average_path,'o-', label='$r_1$')
plt.xlabel('assortivity')
plt.ylabel('average path length')
plt.savefig('results/average_length_path.png', dpi=500, bbox_inches='tight')
plt.show()