1>>> import networkx

2>>> networkx.__version__

3'1.11'

1pip install --upgrade networkx

1sudo apt-get install graphviz

2sudo apt-get install graphviz libgraphviz-dev pkg-config

3sudo apt-get install python-pip python-virtualenv

4pip install pygraphviz

1pip install opencv-python #安装非常慢，用下面的方式，从清华源下载

2pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-python

1import networkx as nx

2G=nx.Graph

3G=nx.DiGraph

4G=nx.MultiGraph

5G=nx.MultiDiGraph

6G.clear #清空图

1import import networkx as nx #导入networkx包

2import matplotlib.pyplot as plt

3G = nx.random_graphs.barabasi_albert_graph(100,1) #生成一个BA无标度网络G

4nx.draw(G) #绘制网络G

5plt.savefig("ba.png") #输出方式1: 将图像存为一个png格式的图片文件

6plt.show #输出方式2: 在窗口中显示这幅图像

1draw(G，[pos,ax,hold])

2draw_networkx(G，[pos,with_labels])

3draw_networkx_nodes(G,pos,[nodelist])绘制网络G的节点图

4draw_networkx_edges(G,pos[edgelist])绘制网络G的边图

5draw_networkx_edge_labels(G, pos[, …]) 绘制网络G的边图，边有label

6—有layout 布局画图函数的分界线—

7draw_circular(G, **kwargs) Draw the graph G with a circular layout.

8draw_random(G, **kwargs) Draw the graph G with a random layout.

9draw_spectral(G, **kwargs)Draw the graph G with a spectral layout.

10draw_spring(G, **kwargs)Draw the graph G with a spring layout.

11draw_shell(G, **kwargs) Draw networkx graph with shell layout.

12draw_graphviz(G[, prog])Draw networkx graph with graphviz layout.

1import networkx as nx

2import matplotlib.pyplot as plt

3G = nx.Graph # 建立一个空的无向图G

4#增加节点

5G.add_node('a') # 添加一个节点1

6G.add_nodes_from(['b', 'c', 'd', 'e']) # 加点集合

7G.add_cycle(['f', 'g', 'h', 'j']) # 加环

8H = nx.path_graph(10) # 返回由10个节点的无向图

9G.add_nodes_from(H) # 创建一个子图H加入G

10G.add_node(H) # 直接将图作为节点

11

12nx.draw(G, with_labels=True,node_color='red')

13plt.show

14

15#访问节点

16print('图中所有的节点', G.nodes)

17#图中所有的节点 [0, 1, 2, 3, 'a', 'c', 'f', 7, 8, 9, , 'g', 'e', 'h', 'b', 4, 6, 5, 'j', 'd']

18

19print('图中节点的个数', G.number_of_nodes)

20#图中节点的个数 20

21

22#删除节点

23G.remove_node(1) #删除指定节点

24G.remove_nodes_from(['b','c','d','e']) #删除集合中的节点

1import networkx as nx

2import matplotlib.pyplot as plt

3

4#添加边方法1

5

6F = nx.Graph # 创建无向图

7F.add_edge(11,12) #一次添加一条边

8

9#添加边方法2

10e=(13,14) #e是一个元组

11F.add_edge(*e) #这是python中解包裹的过程

12

13#添加边方法3

14F.add_edges_from([(1,2),(1,3)]) #通过添加list来添加多条边

15

16H = nx.path_graph(10) #返回由10个节点的无向图

17#通过添加任何ebunch来添加边

18F.add_edges_from(H.edges) #不能写作F.add_edges_from(H)

19

20nx.draw(F, with_labels=True)

21plt.show

22

23#访问边

24print('图中所有的边', F.edges)

25# 图中所有的边 [(0, 1), (1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (11, 12), (13, 14)]

26

27print('图中边的个数', F.number_of_edges)

28# 图中边的个数 12

29

30

31

32#删除边

33F.remove_edge(1,2)

34F.remove_edges_from([(11,12), (13,14)])

35

36nx.draw(F, with_labels=True)

37plt.show

1import networkx as nx

2import matplotlib.pyplot as plt

3

4#快速遍历每一条边，可以使用邻接迭代器实现，对于无向图，每一条边相当于两条有向边

5FG = nx.Graph

6FG.add_weighted_edges_from([(1,2,0.125), (1,3,0.75), (2,4,1.2), (3,4,0.275)])

7for n, nbrs in FG.adjacency_iter:

8 for nbr, eattr in nbrs.items:

9 data = eattr['weight']

10 print('(%d, %d, %0.3f)' % (n,nbr,data))

11 # (1, 2, 0.125)

12 # (1, 3, 0.750)

13 # (2, 1, 0.125)

14 # (2, 4, 1.200)

15 # (3, 1, 0.750)

16 # (3, 4, 0.275)

17 # (4, 2, 1.200)

18 # (4, 3, 0.275)

19

20print('***********************************')

21

22#筛选weight小于0.5的边：

23FG = nx.Graph

24FG.add_weighted_edges_from([(1,2,0.125), (1,3,0.75), (2,4,1.2), (3,4,0.275)])

25for n, nbrs in FG.adjacency_iter:

26 for nbr, eattr in nbrs.items:

27 data = eattr['weight']

28 if data < 0.5:

29 print('(%d, %d, %0.3f)' % (n,nbr,data))

30 # (1, 2, 0.125)

31 # (2, 1, 0.125)

32 # (3, 4, 0.275)

33 # (4, 3, 0.275)

34

35print('***********************************')

36

37#一种方便的访问所有边的方法:

38for u,v,d in FG.edges(data = 'weight'):

39 print((u,v,d))

40 # (1, 2, 0.125)

41 # (1, 3, 0.75)

42 # (2, 4, 1.2)

43 # (3, 4, 0.275)

1#图的属性

2

3import networkx as nx

4

5G = nx.Graph(day='Monday') #可以在创建图时分配图的属性

6print(G.graph)

7

8G.graph['day'] = 'Friday' #也可以修改已有的属性

9print(G.graph)

10

11G.graph['name'] = 'time' #可以随时添加新的属性到图中

12print(G.graph)

13

14输出：

15{'day': 'Monday'}

16{'day': 'Friday'}

17{'day': 'Friday', 'name': 'time'}

1#节点的属性

2import networkx as nx

3

4G = nx.Graph(day='Monday')

5G.add_node(1, index='1th') #在添加节点时分配节点属性

6# print(G.node(data=True)) #TypeError: 'dict' object is not callable

7print(G.node)

8#{1: {'index': '1th'}}

9

10

11G.node[1]['index'] = '0th' #通过G.node来添加或修改属性

12print(G.node)

13# {1: {'index': '0th'}}

14

15

16G.add_nodes_from([2,3], index='2/3th') #从集合中添加节点时分配属性

17print(G.node)

18# {1: {'index': '0th'}, 2: {'index': '2/3th'}, 3: {'index': '2/3th'}}

1#边的属性

2import networkx as nx

3

4G = nx.Graph(day='manday')

5G.add_edge(1,2,weight=10) #在添加边时分配属性

6print(G.edges(data=True))

7#[(1, 2, {'weight': 10})]

8

9G.add_edges_from([(1,3), (4,5)], len=22) #从集合中添加边时分配属性

10print(G.edges(data='len'))

11# [(1, 2, None), (1, 3, 22), (4, 5, 22)]

12

13G.add_edges_from([(3,4,{'hight':10}),(1,4,{'high':'unknow'})])

14print(G.edges(data=True))

15# [(1, 2, {'weight': 10}), (1, 3, {'len': 22}), (1, 4, {'high': 'unknow'}), (3, 4, {'hight': 10}), (4, 5, {'len': 22})]

16

17

18G[1][2]['weight'] = 100000 #通过G来添加或修改属性

19print(G.edges(data=True))

20# [(1, 2, {'weight': 100000}), (1, 3, {'len': 22}), (1, 4, {'high': 'unknow'}), (3, 4, {'hight': 10}), (4, 5, {'len': 22})]

1#有向图转化成无向图

2

3H=DG.to_undirected

4#或者

5H=nx.Graph(DG)

6

7#无向图转化成有向图

8

9F = H.to_directed

10#或者

11F = nx.DiGraph(H)

1import networkx as nx

2import matplotlib.pyplot as plt

3

4G = nx.DiGraph

5G.add_node(1)

6G.add_node(2)

7G.add_nodes_from([3,4,5,6])

8G.add_cycle([1,2,3,4])

9G.add_edge(1,3)

10G.add_edges_from([(3,5),(3,6),(6,7)])

11nx.draw(G,node_color = 'red')

12plt.savefig("youxiangtu.png")

13plt.show

1from __future__ import division

2import matplotlib.pyplot as plt

3import networkx as nx

4

5G = nx.generators.directed.random_k_out_graph(10, 3, 0.5)

6pos = nx.layout.spring_layout(G)

7

8node_sizes = [3 + 10 * i for i in range(len(G))]

9M = G.number_of_edges

10edge_colors = range(2, M + 2)

11edge_alphas = [(5 + i) / (M + 4) for i in range(M)]

12

13nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color='blue')

14edges = nx.draw_networkx_edges(G, pos, node_size=node_sizes, arrowstyle='->',

15 arrowsize=10, edge_color=edge_colors,

16 edge_cmap=plt.cm.Blues, width=2)

17# set alpha value for each edge

18for i in range(M):

19 edges[i].set_alpha(edge_alphas[i])

20

21ax = plt.gca

22ax.set_axis_off

23plt.savefig("directed.jpg")

24plt.show

1import matplotlib.pyplot as plt

2import networkx as nx

3

4try:

5 import pygraphviz

6 from networkx.drawing.nx_agraph import graphviz_layout

7except ImportError:

8 try:

9 import pydot

10 from networkx.drawing.nx_pydot import graphviz_layout

11 except ImportError:

12 raise ImportError("This example needs Graphviz and either "

13 "PyGraphviz or pydot")

14

15G = nx.balanced_tree(3, 5)

16pos = graphviz_layout(G, prog='twopi', args='')

17plt.figure(figsize=(8, 8))

18nx.draw(G, pos, node_size=20, alpha=0.5, node_color="blue