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, andstudy 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 nxG = 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 nxG=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 nxG=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 nxG=nx.Graph()G.add_node(1)G.add_nodes_from([2,3])H=nx.path_graph(10) ?????# type(H) networkx.classes.graph.GraphG.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 nximport matplotlib.pyplot as pltG = nx.Graph() ????????????????#建立一个空的无向图GG.add_node(1) ?????????????????#添加一个节点1G.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：1nx.draw(G)plt.savefig("wuxiangtu.png")plt.show()

nodes: [1, 2, 3]edges: [(2, 3)]number of edges: 1

#-*- coding:utf8-*- import networkx as nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = nx.Graph() ???????????????????????????????????????#建立一个空的无向图GG.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 nximport matplotlib.pyplot as pltG = 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 nximport 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 nxG=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 nximport matplotlib.pyplot as plt#G = nx.path_graph(4, create_using=nx.Graph())#0 1 2 3G = 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->3G.add_path([7, 8, 3]) ?#生成有向边：7->8->3for 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 nximport matplotlib.pyplot as plt#G = nx.path_graph(4, create_using=nx.Graph())#0 1 2 3G = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = 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 nximport matplotlib.pyplot as pltG = nx.DiGraph()def flt_func_draw(): ???flt_func = lambda d: d[‘id‘] != 1 ???return flt_funcroad_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 nximport matplotlib.pyplot as pltG = 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 ?{}

画图小技巧

%pylab inlinempl.rcParams[‘font.sans-serif‘] = [‘SimHei‘] ?# 指定默认字体mpl.rcParams[‘axes.unicode_minus‘] = False ?# 解决保存图像是负号 ‘-‘ 显示为方块的问题import networkx as nxg=nx.Graph()g.add_edge(‘张三‘,‘李四‘)g.add_edge(‘张三‘,‘王五‘)nx.draw(g,with_labels=True)plt.show()

Populating the interactive namespace from numpy and matplotlib

networkx 有四种图 Graph 、DiGraph、MultiGraph、MultiDiGraph，分别为无多重边无向图、无多重边有向图、有多重边无向图、有多重边有向图。

import networkx as nxG = nx.Graph() ?# 创建空的网络图G = nx.DiGraph()G = nx.MultiGraph()G = nx.MultiDiGraph()G.add_node(‘a‘)#添加点aG.add_node(1,1)#用坐标来添加点G.add_edge(‘x‘,‘y‘)#添加边,起点为x，终点为yG.add_weight_edges_from([(‘x‘,‘y‘,1.0)])#第三个输入量为权值#也可以L = [[(‘a‘,‘b‘,5.0),(‘b‘,‘c‘,3.0),(‘a‘,‘c‘,1.0)]]G.add_weight_edges_from([(L)])nx.draw(G)plt.show() ??# 图像显示

为了让图形更精美我们详解 nx.draw()

nx.draw(G, pos=None, ax=None, **kwds)

• pos 指的是布局，主要有 spring_layout , random_layout，circle_layout，shell_layout。
• node_color 指节点颜色，有 rbykw ,同理 edge_color.
• with_labels 指节点是否显示名字
• size 表示大小
• font_color 表示字的颜色。

import networkx as nximport numpy as npimport matplotlib.pyplot as pltG = nx.Graph()G.add_edges_from( ???[(‘A‘, ‘B‘), (‘A‘, ‘C‘), (‘D‘, ‘B‘), (‘E‘, ‘C‘), (‘E‘, ‘F‘), ????(‘B‘, ‘H‘), (‘B‘, ‘G‘), (‘B‘, ‘F‘), (‘C‘, ‘G‘)])val_map = {‘A‘: 1.0, ??????????‘D‘: 0.5714285714285714, ??????????‘H‘: 0.0}values = [val_map.get(node, 0.25) for node in G.nodes()]nx.draw(G, cmap = plt.get_cmap(‘jet‘), node_color = values)plt.show()

import networkx as nximport matplotlib.pyplot as pltG = nx.DiGraph()G.add_edges_from( ???[(‘A‘, ‘B‘), (‘A‘, ‘C‘), (‘D‘, ‘B‘), (‘E‘, ‘C‘), (‘E‘, ‘F‘), ????(‘B‘, ‘H‘), (‘B‘, ‘G‘), (‘B‘, ‘F‘), (‘C‘, ‘G‘)])val_map = {‘A‘: 1.0, ??????????‘D‘: 0.5714285714285714, ??????????‘H‘: 0.0}values = [val_map.get(node, 0.25) for node in G.nodes()]# Specify the edges you want herered_edges = [(‘A‘, ‘C‘), (‘E‘, ‘C‘)]edge_colours = [‘black‘ if not edge in red_edges else ‘red‘ ???????????????for edge in G.edges()]black_edges = [edge for edge in G.edges() if edge not in red_edges]# Need to create a layout when doing# separate calls to draw nodes and edgespos = nx.spring_layout(G)nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap(‘jet‘), ???????????????????????node_color = values, node_size = 500)nx.draw_networkx_labels(G, pos)nx.draw_networkx_edges(G, pos, edgelist=red_edges, edge_color=‘r‘, arrows=True)nx.draw_networkx_edges(G, pos, edgelist=black_edges, arrows=False)plt.show()

import networkx as nximport numpy as npimport matplotlib.pyplot as pltimport pylabG = nx.DiGraph()G.add_edges_from([(‘A‘, ‘B‘),(‘C‘,‘D‘),(‘G‘,‘D‘)], weight=1)G.add_edges_from([(‘D‘,‘A‘),(‘D‘,‘E‘),(‘B‘,‘D‘),(‘D‘,‘E‘)], weight=2)G.add_edges_from([(‘B‘,‘C‘),(‘E‘,‘F‘)], weight=3)G.add_edges_from([(‘C‘,‘F‘)], weight=4)val_map = {‘A‘: 1.0, ??????????????????‘D‘: 0.5714285714285714, ?????????????????????????????‘H‘: 0.0}values = [val_map.get(node, 0.45) for node in G.nodes()]edge_labels=dict([((u,v,),d[‘weight‘]) ????????????????for u,v,d in G.edges(data=True)])red_edges = [(‘C‘,‘D‘),(‘D‘,‘A‘)]edge_colors = [‘black‘ if not edge in red_edges else ‘red‘ for edge in G.edges()]pos=nx.spring_layout(G)nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)nx.draw(G,pos, node_color = values, node_size=1500,edge_color=edge_colors,edge_cmap=plt.cm.Reds)pylab.show()

NetworkX

原文地址：https://www.cnblogs.com/q735613050/p/7441069.html