python复杂网络 学习笔记

networkx库

pip install --upgrade networkx

点和边示例：

  

   

    

     

    
    

     

      import networkx as nx
     
    
   

    

     

    
    

     

      import matplotlib.pyplot as plt
     
    
   

    

     

    
    

     

      G = nx.Graph()  #初始化一个图
     
    
   

    

     

    
    

     

      G.add_node('a')
     
    
   

    

     

    
    

     

      G.add_node('b')
     
    
   

    

     

    
    

     

      G.add_node('c')
     
    
   

    

     

    
    

     

      G.add_node('d')
     
    
   

    

     

    
    

     

      G.add_node('e')
     
    
   

    

     

    
    

     

      G.add_edge('a','b') #连接a、b得到ab边
     
    
   

    

     

    
    

     

      G.add_edge('a','d')
     
    
   

    

     

    
    

     

      G.add_edge('a','e')
     
    
   

    

     

    
    

     

      G.add_edge('a','c')
     
    
   

    

     

    
    

     

      nx.draw(G,with_labels=True)
     
    
   

    

     

    
    

     

      plt.show()
     
    
  
 

规则图：

  

   

    

     

    
    

     

      import networkx as nx
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import matplotlib.pyplot as plt
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      RG = nx.random_graphs.random_regular_graph(3,20)  #生成包含20个节点、每个节点有3个邻居的规则图RG
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      pos = nx.spectral_layout(RG)          #定义一个布局，此处采用了spectral布局方式，后变还会介绍其它布局方式，注意图形上的区别
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      nx.draw(RG,pos,with_labels=False,node_size = 30)  #绘制规则图的图形，with_labels决定节点是非带标签(编号)，node_size是节点的直径
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      plt.show()  #显示图形
     
    
  
 

无向图示例：

  

   

    

     

    
    

     

      import networkx as nx
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import matplotlib.pyplot as plt
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # BA scale-free degree network
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # generalize BA network which has 20 nodes, m = 1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      BA = nx.random_graphs.barabasi_albert_graph(20, 1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # spring layout
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      pos = nx.spring_layout(BA)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      nx.draw(BA, pos, with_labels = False, node_size = 30)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      plt.show()
     
    
  
 

  

   

    

     

    
    

     

      # 导入相关依赖
     
    
   

    

     

    
    

     

      from matplotlib import pyplot as plt
     
    
   

    

     

    
    

     

      import networkx as nx
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 生成随机数据
     
    
   

    

     

    
    

     

      G = nx.erdos_renyi_graph(50,0.5)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 指定画布大小
     
    
   

    

     

    
    

     

      plt.figure(figsize=(18,18))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 生成新的图
     
    
   

    

     

    
    

     

      G_new = nx.Graph()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 依据图中边的数量，生成同样长度的随机权重值
     
    
   

    

     

    
    

     

      weightList = {}
     
    
   

    

     

    
    

     

      for i in range(len(G.edges())+1):
     
    
   

    

     

    
    

     

          weightList[i] = np.random.rand()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 将生成的随机权重复制给G_new图
     
    
   

    

     

    
    

     

      i = 0
     
    
   

    

     

    
    

     

      for edge in G.edges():
     
    
   

    

     

    
    

     

          i += 1
     
    
   

    

     

    
    

     

          G_new.add_edges_from([(edge[0], edge[1], {'weight': weightList[i]})])
     
    
   

    

     

    
    

     

      # 绘制G_new图
     
    
   

    

     

    
    

     

      nx.draw_networkx(G_new)
     
    
   

    

     

    
    

     

      plt.show()
     
    
  
 

文章来源: blog.csdn.net，作者：AI视觉网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/122783422