先决条件:社交网络简介,鄂尔多斯-人一模型
Erdos Renyi模型用于在社交网络上创建随机网络或图形。在Erdos Reny模型中,每个边缘都有固定的概率出现,而与网络中的边缘无关地不存在。
使用Erdos-Renyi模型实施社交网络:
步骤1)导入必要的模块,例如networkx , matplotlib.pyplot和random模块。
Python3
# Import Required modules
import networkx as nx
import matplotlib.pyplot as plt
import random
Python3
# Distribution graph for Erdos_Renyi model
def distribution_graph(g):
print(nx.degree(g))
all_node_degree = list(dict((nx.degree(g))).values())
unique_degree = list(set(all_node_degree))
unique_degree.sort()
nodes_with_degree = []
for i in unique_degree:
nodes_with_degree.append(all_node_degree.count(i))
plt.plot(unique_degree, nodes_with_degree)
plt.xlabel("Degrees")
plt.ylabel("No. of nodes")
plt.title("Degree distribution")
plt.show()
Python3
# Take N number of nodes as input
print("Enter number of nodes")
N = int(input())
Python3
# Take P probability value for edges
print("Enter value of probability of every node")
P = float(input())
Python3
# Create an empty graph object
g = nx.Graph()
# Adding nodes
g.add_nodes_from(range(1, N + 1))
Python3
# Add edges to the graph randomly.
for i in g.nodes():
for j in g.nodes():
if (i < j):
# Take random number R.
R = random.random()
# Check if R
Python3
# Display connection between nodes
distribution_graph(g)
Python3
# Implementation of Erdos-Renyi Model on a Social Network
# Import Required modules
import networkx as nx
import matplotlib.pyplot as plt
import random
# Distribution graph for Erdos_Renyi model
def distribution_graph(g):
print(nx.degree(g))
all_node_degree = list(dict((nx.degree(g))).values())
unique_degree = list(set(all_node_degree))
unique_degree.sort()
nodes_with_degree = []
for i in unique_degree:
nodes_with_degree.append(all_node_degree.count(i))
plt.plot(unique_degree, nodes_with_degree)
plt.xlabel("Degrees")
plt.ylabel("No. of nodes")
plt.title("Degree distribution")
plt.show()
# Take N number of nodes from user
print("Enter number of nodes")
N = int(input())
# Take P probability value for edges
print("Enter value of probability of every node")
P = float(input())
# Create an empty graph object
g = nx.Graph()
# Adding nodes
g.add_nodes_from(range(1, N + 1))
# Add edges to the graph randomly.
for i in g.nodes():
for j in g.nodes():
if (i < j):
# Take random number R.
R = random.random()
# Check if R
步骤2)为模型创建分布图。
Python3
# Distribution graph for Erdos_Renyi model
def distribution_graph(g):
print(nx.degree(g))
all_node_degree = list(dict((nx.degree(g))).values())
unique_degree = list(set(all_node_degree))
unique_degree.sort()
nodes_with_degree = []
for i in unique_degree:
nodes_with_degree.append(all_node_degree.count(i))
plt.plot(unique_degree, nodes_with_degree)
plt.xlabel("Degrees")
plt.ylabel("No. of nodes")
plt.title("Degree distribution")
plt.show()
步骤3)从用户那里获取N即节点数。
Python3
# Take N number of nodes as input
print("Enter number of nodes")
N = int(input())
步骤4)现在取P,即来自用户的边缘概率。
Python3
# Take P probability value for edges
print("Enter value of probability of every node")
P = float(input())