如何使用python中的networkx来生成一个图

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 16 20:13:42 2019

@author: Administrator
"""

"""

这个函数的作用是将一个矩阵给转换成一个图，
矩阵以多维列表的形式存在，即列表的列表
此处的转换是针对无向图

根据邻接矩阵得到图之后，我们就可以调用networkx
里面的各种函数来分析图的性质，比如度分布，
平均路径程度，聚类系数等一系列图的拓扑性质

"""

import networkx as nx


def matrix_to_graph():

  G = nx.Graph()
  
  #matrix为邻接矩阵，以多维列表的形式存在
  matrix = [[0, 1, 1],[1,0,1],[1,1,0]]
  
  nodes = range(len(matrix))
  G.add_nodes_from(nodes)
 
  for i in range(len(matrix)):
    for j in range(len(matrix)):
      if(matrix[i][j] == 1):
		G.add_edge(i, j)
        
  position = nx.circular_layout(G)
  nx.draw_networkx_nodes(G,position, nodelist=nodes, node_color="r")
  nx.draw_networkx_edges(G,position)
  nx.draw_networkx_labels(G,position)
  print(nx.to_numpy_matrix(G))

matrix_to_graph()

import networkx as nx   #导入networkx包
import random            #导入random包
import matplotlib.pyplot as plt #导入画图工具包

G = nx.Graph()            #建立无向图
H = nx.path_graph(100)    #添加节点
G.add_nodes_from(H)        #添加节点

def rand_edge(vi,vj,p=0.2):        #默认概率p=0.1
    probability =random.random()#生成随机小数
    if(probability<p):            #如果小于p
        G.add_edge(vi,vj)          #连接vi和vj节点

i=0
while (i<100):
    j=0
    while(j<i):
            rand_edge(i,j)        #调用rand_edge()
            j +=1
    i +=1

number_components = nx.number_connected_components(G) 
largest_components = max(nx.connected_components(G), key=len)
print("最大连通子图:" + str(largest_components))
print("最大连通子图长度："+ str(len(largest_components)))
print("连通子图个数: "+str(nx.number_connected_components(G)))

nx.degree(G)
DVweight = G.degree()
degree_sum = sum(span for n, span in DVweight)         #各节点度数之和
degree_max = max(span for n, span in DVweight)        #节点最大度数

import networkx as nx     #导入networkx包
import random			  #导入random包
import matplotlib.pyplot as plt

G = nx.Graph()
H = nx.path_graph(100)
G.add_nodes_from(H)

def rand_edge(vi,vj,p=0.2):
    probability =random.random()
    if(probability<p):
        G.add_edge(vi,vj)   
i=0
while (i<100):
    j=0
    while(j<i):
            rand_edge(i,j)
            j +=1
    i +=1

number_components = nx.number_connected_components(G) 
largest_components = max(nx.connected_components(G), key=len)
nx.degree(G)
DVweight = G.degree()
degree_sum = sum(span for n, span in DVweight) 		#各节点度数之和
degree_max = max(span for n, span in DVweight)		#节点最大度数

print("度数之和: " + str(degree_sum))
print("节点最大度数：" + str(degree_max))
print("最大连通子图:" + str(largest_components))
print("最大连通子图长度："+ str(len(largest_components)))
print("连通子图个数: "+str(nx.number_connected_components(G)))
nx.draw_networkx(G, with_labels=True)
plt.show()