常用网站：

官方文档 Github (latest development)

NetworkX官方介绍：

========

NetworkX (NX) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. <https://networkx.lanl.gov/> Just write in Python >>> import networkx as nx >>> G=nx.Graph() >>> G.add_edge(1,2) >>> G.add_node(42) >>> print(sorted(G.nodes())) [1, 2, 42] >>> print(sorted(G.edges())) [(1, 2)] 用来处理无向图、有向图、多重图的Python数据结构 包含许多标准的图算法 包括网络结构和分析方法 用于产生经典图、随机图和综合网络 节点可以是任何事物（如text, images, XML records） 边能保存任意起算值（如weights, time-series） 开源证书 BSD license 很好的测试结果：超过1800个单元测试，90%的结点覆盖 从Python获得的额外优势：快速原型开发方法，容易学，支持多平台。 import networkx as nx G = nx.Graph() G.add_edge('A', 'B', weight=4) G.add_edge('B', 'D', weight=2) G.add_edge('A', 'C', weight=3) G.add_edge('C', 'D', weight=4) nx.shortest_path(G, 'A', 'D', weight='weight') ['A', 'B', 'D'] import networkx as nx G=nx.Graph() G.add_edge(1,2) G.add_node(42) print(sorted(G.nodes())) print(sorted(G.edges())) [1, 2, 42] [(1, 2)]

创建图

import networkx as nx G=nx.Graph() Graph是结点（向量）与确定的结点对（称作边、链接等）的集合。 在Networkx中，结点可以是任何可哈希的对象，如文本字符串、图片、XML对象、其他图，自定义对象等。 注意:python的None对象不应该用作结点，

可哈希的：一个对象在它的生存期从来不会被改变（拥有一个哈希方法），能和其他对象区别（有比较方法）

结点

Neatworkx包含很多图生成器函数和工具，可用来以多种格式来读写图。

一次增加一个节点：

G.add_node(1)

用序列增加一系列的节点

G.add_nodes_from([2,3])

增加 nbunch结点。

nbunch是可迭代的结点容器 （序列、集合、图、文件等），其本身不是图的某个节点

import networkx as nx G=nx.Graph() G.add_node(1) G.add_nodes_from([2,3]) H=nx.path_graph(10) # type(H) networkx.classes.graph.Graph G.add_nodes_from(H) # 这是将H中的许多结点作为G的节点 G.add_node(H) # 这是将H作为G中的一个节点 #查看结点 G.node {1: {}, 2: {}, 3: {}, 0: {}, 4: {}, 5: {}, 6: {}, 7: {}, 8: {}, 9: {}, <networkx.classes.graph.Graph at 0x18ecbb89940>: {}} #G能够一次增加一条边 G.add_edge(1,2) #只能增加边，有属性，除非指定属性名和值“属性名=值” e=(2,3) G.add_edge(*e) #注意！ G.add_edge(e)会报错！G.add_edge(e) #用序列增加一系列结点 G.add_edges_from([(1,2),(1,3)]) #增加 ebunch边。ebunch：包含边元组的容器，比如序列、迭代器、文件等 #这个元组可以是2维元组或 三维元组 （node1，node2，an_edge_attribute_dictionary）,an_edge_attribute_dictionary比如： #{‘weight’:3.1415} G.add_edges_from(H.edges())

删除

G.remove_node(),G.remove_nodes_from() G.remove_edge(),G.remove_edges_from() G.remove_node(H) #删除不存在的东西会报错 #移除所有的节点和边 G.clear() G.add_edges_from([(1,2),(1,3)]) G.add_node(1) G.add_edge(1,2) G.add_node("spam") G.add_nodes_from("spam") # adds 4 nodes: 's', 'p', 'a', 'm' G.edges(),G.nodes(),G.number_of_edges(),G.number_of_nodes() import networkx as nx <module 'networkx' from 'D:\\ProgramData\\Anaconda3\\lib\\site-packages\\networkx\\__init__.py'>

无向图

import networkx as nx import matplotlib.pyplot as plt G = nx.Graph() #建立一个空的无向图G G.add_node(1) #添加一个节点1 G.add_edge(2,3) #添加一条边2-3（隐含着添加了两个节点2、3） G.add_edge(3,2) #对于无向图，边3-2与边2-3被认为是一条边 print ("nodes:", G.nodes()) #输出全部的节点： [1, 2, 3] print ("edges:", G.edges()) #输出全部的边：[(2, 3)] print ("number of edges:", G.number_of_edges()) #输出边的数量：1 nx.draw(G) plt.savefig("wuxiangtu.png") plt.show() nodes: [1, 2, 3] edges: [(2, 3)] number of edges: 1

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() G.add_node(1) G.add_node(2) #加点 G.add_nodes_from([3,4,5,6]) #加点集合 G.add_cycle([1,2,3,4]) #加环 G.add_edge(1,3) G.add_edges_from([(3,5),(3,6),(6,7)]) #加边集合 nx.draw(G) plt.savefig("youxiangtu.png") plt.show()

有向图

#!-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() G.add_node(1) G.add_node(2) G.add_nodes_from([3,4,5,6]) G.add_cycle([1,2,3,4]) G.add_edge(1,3) G.add_edges_from([(3,5),(3,6),(6,7)]) nx.draw(G) plt.savefig("youxiangtu.png") plt.show()

注：有向图和无向图可以互相转换，使用函数：

Graph.to_undirected() Graph.to_directed() # 例子中把有向图转化为无向图 import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() G.add_node(1) G.add_node(2) G.add_nodes_from([3,4,5,6]) G.add_cycle([1,2,3,4]) G.add_edge(1,3) G.add_edges_from([(3,5),(3,6),(6,7)]) G = G.to_undirected() nx.draw(G) plt.savefig("wuxiangtu.png") plt.show()

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() road_nodes = {'a': 1, 'b': 2, 'c': 3} #road_nodes = {'a':{1:1}, 'b':{2:2}, 'c':{3:3}} road_edges = [('a', 'b'), ('b', 'c')] G.add_nodes_from(road_nodes.items()) G.add_edges_from(road_edges) nx.draw(G) plt.savefig("youxiangtu.png") plt.show()

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() #road_nodes = {'a': 1, 'b': 2, 'c': 3} road_nodes = {'a':{1:1}, 'b':{2:2}, 'c':{3:3}} road_edges = [('a', 'b'), ('b', 'c')] G.add_nodes_from(road_nodes.items()) G.add_edges_from(road_edges) nx.draw(G) plt.savefig("youxiangtu.png") plt.show()

加权图

有向图和无向图都可以给边赋予权重，用到的方法是add_weighted_edges_from，它接受1个或多个三元组[u,v,w]作为参数， 其中u是起点，v是终点，w是权重

#!-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.Graph() #建立一个空的无向图G G.add_edge(2,3) #添加一条边2-3（隐含着添加了两个节点2、3） G.add_weighted_edges_from([(3, 4, 3.5),(3, 5, 7.0)]) #对于无向图，边3-2与边2-3被认为是一条边 print (G.get_edge_data(2, 3)) print (G.get_edge_data(3, 4)) print (G.get_edge_data(3, 5)) nx.draw(G) plt.savefig("wuxiangtu.png") plt.show() {} {'weight': 3.5} {'weight': 7.0}

import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph()

经典图论算法计算

计算1：求无向图的任意两点间的最短路径

# -*- coding: cp936 -*- import networkx as nx import matplotlib.pyplot as plt #计算1：求无向图的任意两点间的最短路径 G = nx.Graph() G.add_edges_from([(1,2),(1,3),(1,4),(1,5),(4,5),(4,6),(5,6)]) path = nx.all_pairs_shortest_path(G) print(path[1]) {1: [1], 2: [1, 2], 3: [1, 3], 4: [1, 4], 5: [1, 5], 6: [1, 4, 6]}

计算2：找图中两个点的最短路径

import networkx as nx G=nx.Graph() G.add_nodes_from([1,2,3,4]) G.add_edge(1,2) G.add_edge(3,4) try: n=nx.shortest_path_length(G,1,4) print (n) except nx.NetworkXNoPath: print ('No path') No path

强连通、弱连通

强连通：有向图中任意两点v1、v2间存在v1到v2的路径（path）及v2到v1的路径。 弱联通：将有向图的所有的有向边替换为无向边，所得到的图称为原图的基图。如果一个有向图的基图是连通图，则有向图是弱连通图。

距离

例1：弱连通

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt #G = nx.path_graph(4, create_using=nx.Graph()) #0 1 2 3 G = nx.path_graph(4, create_using=nx.DiGraph()) #默认生成节点0 1 2 3，生成有向变0->1,1->2,2->3 G.add_path([7, 8, 3]) #生成有向边：7->8->3 for c in nx.weakly_connected_components(G): print (c) print ([len(c) for c in sorted(nx.weakly_connected_components(G), key=len, reverse=True)]) nx.draw(G) plt.savefig("youxiangtu.png") plt.show() {0, 1, 2, 3, 7, 8} [6]

例2：强连通

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt #G = nx.path_graph(4, create_using=nx.Graph()) #0 1 2 3 G = nx.path_graph(4, create_using=nx.DiGraph()) G.add_path([3, 8, 1]) #for c in nx.strongly_connected_components(G): # print c # #print [len(c) for c in sorted(nx.strongly_connected_components(G), key=len, reverse=True)] con = nx.strongly_connected_components(G) print (con) print (type(con)) print (list(con)) nx.draw(G) plt.savefig("youxiangtu.png") plt.show() <generator object strongly_connected_components at 0x0000018ECC82DD58> <class 'generator'> [{8, 1, 2, 3}, {0}]

子图

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() G.add_path([5, 6, 7, 8]) sub_graph = G.subgraph([5, 6, 8]) #sub_graph = G.subgraph((5, 6, 8)) #ok 一样 nx.draw(sub_graph) plt.savefig("youxiangtu.png") plt.show()

条件过滤

原图

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() road_nodes = {'a':{'id':1}, 'b':{'id':1}, 'c':{'id':3}, 'd':{'id':4}} road_edges = [('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'd')] G.add_nodes_from(road_nodes) G.add_edges_from(road_edges) nx.draw(G) plt.savefig("youxiangtu.png") plt.show()

过滤函数

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() def flt_func_draw(): flt_func = lambda d: d['id'] != 1 return flt_func road_nodes = {'a':{'id':1}, 'b':{'id':1}, 'c':{'id':3}, 'd':{'id':4}} road_edges = [('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'd')] G.add_nodes_from(road_nodes.items()) G.add_edges_from(road_edges) flt_func = flt_func_draw() part_G = G.subgraph(n for n, d in G.nodes_iter(data=True) if flt_func(d)) nx.draw(part_G) plt.savefig("youxiangtu.png") plt.show()

pred，succ

#-*- coding:utf8-*- import networkx as nx import matplotlib.pyplot as plt G = nx.DiGraph() road_nodes = {'a':{'id':1}, 'b':{'id':1}, 'c':{'id':3}} road_edges = [('a', 'b'), ('a', 'c'), ('c', 'd')] G.add_nodes_from(road_nodes.items()) G.add_edges_from(road_edges) print( G.nodes()) print (G.edges()) print ("a's pred ", G.pred['a']) print ("b's pred ", G.pred['b']) print ("c's pred ", G.pred['c']) print ("d's pred ", G.pred['d']) print ("a's succ ", G.succ['a']) print ("b's succ ", G.succ['b']) print ("c's succ ", G.succ['c']) print ("d's succ ", G.succ['d']) nx.draw(G) plt.savefig("wuxiangtu.png") plt.draw() ['a', 'b', 'c', 'd'] [('a', 'b'), ('a', 'c'), ('c', 'd')] a's pred {} b's pred {'a': {}} c's pred {'a': {}} d's pred {'c': {}} a's succ {'b': {}, 'c': {}} b's succ {} c's succ {'d': {}} d's succ {}