python-networkx绘制复杂网络关系图

2017-09-05

g.number_of_nodes() #查看点的数量
g.number_of_edges() #查看边的数量
g.nodes() #返回所有点的信息(list)
g.edges() #返回所有边的信息(list中每个元素是一个tuple)
g.neighbors(1) #所有与1这个点相连的点的信息以列表的形式返回
g[1] #查看所有与1相连的边的属性，格式输出：{0: {}, 2: {}} 表示1和0相连的边没有设置任何属性（也就是{}没有信息），同理1和2相连的边也没有任何属性

method	explanation
`Graph.has_node`(n)	Return True if the graph contains the node n.
`Graph.__contains__`(n)	Return True if n is a node, False otherwise.
`Graph.has_edge`(u, v)	Return True if the edge (u,v) is in the graph.
`Graph.order`()	Return the number of nodes in the graph.
`Graph.number_of_nodes`()	Return the number of nodes in the graph.
`Graph.__len__`()	Return the number of nodes.
`Graph.degree`([nbunch, weight])	Return the degree of a node or nodes.
`Graph.degree_iter`([nbunch, weight])	Return an iterator for (node, degree).
`Graph.size`([weight])	Return the number of edges.
`Graph.number_of_edges`([u, v])	Return the number of edges between two nodes.
`Graph.nodes_with_selfloops`()	Return a list of nodes with self loops.
`Graph.selfloop_edges`([data, default])	Return a list of selfloop edges.
`Graph.number_of_selfloops`()	Return the number of selfloop edges.

5. 图的属性设置

为图赋予初始属性

1 2	g = nx.Graph(day="Monday") g.graph # {'day': 'Monday'}

修改图的属性

1 2	g.graph['day'] = 'Tuesday' g.graph # {'day': 'Tuesday'}

6. 点的属性设置

g.add_node('benz', money=10000, fuel="1.5L")
print g.node['benz'] # {'fuel': '1.5L', 'money': 10000}
print g.node['benz']['money'] # 10000
print g.nodes(data=True) # data默认false就是不输出属性信息，修改为true，会将节点名字和属性信息一起输出

7. 边的属性设置

通过上文中对g[1]的介绍可知边的属性在{}中显示出来，我们可以根据这个秀改变的属性

g.clear()
n = 10
H = nx.path_graph(n)
g.add_nodes_from(H)
g.add_edges_from(H.edges())
g[1][2]['color'] = 'blue'
g.add_edge(1, 2, weight=4.7)
g.add_edges_from([(3,4),(4,5)], color='red')
g.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
g[1][2]['weight'] = 4.7
g.edge[1][2]['weight'] = 4

8. 不同类型的图（有向图Directed graphs , 重边图 Multigraphs）

Directed graphs

DG = nx.DiGraph()
DG.add_weighted_edges_from([(1,2,0.5), (3,1,0.75), (1,4,0.3)]) # 添加带权值的边
print DG.out_degree(1) # 打印结果：2 表示：找到1的出度
print DG.out_degree(1, weight='weight') # 打印结果：0.8 表示：从1出去的边的权值和，这里权值是以weight属性值作为标准，如果你有一个money属性，那么也可以修改为weight='money'，那么结果就是对money求和了
print DG.successors(1) # [2,4] 表示1的后继节点有2和4
print DG.predecessors(1) # [3] 表示只有一个节点3有指向1的连边

Multigraphs

简答从字面上理解就是这种复杂网络图允许你相同节点之间允许出现重边

MG=nx.MultiGraph()
MG.add_weighted_edges_from([(1,2,.5), (1,2,.75), (2,3,.5)])
print MG.degree(weight='weight') # {1: 1.25, 2: 1.75, 3: 0.5}
GG=nx.Graph()
for n,nbrs in MG.adjacency_iter():
    for nbr,edict in nbrs.items():
        minvalue=min([d['weight'] for d in edict.values()])
        GG.add_edge(n,nbr, weight = minvalue)
print nx.shortest_path(GG,1,3) # [1, 2, 3]

9. 图的遍历

g = nx.Graph()
g.add_weighted_edges_from([(1,2,0.125),(1,3,0.75),(2,4,1.2),(3,4,0.375)])
for n,nbrs in g.adjacency_iter(): #n表示每一个起始点，nbrs是一个字典，字典中的每一个元素包含了这个起始点连接的点和这两个点连边对应的属性
  	print n, nbrs
    for nbr,eattr in nbrs.items():
      	# nbr表示跟n连接的点，eattr表示这两个点连边的属性集合，这里只设置了weight，如果你还设置了color，那么就可以通过eattr['color']访问到对应的color元素
        data=eattr['weight']
        if data<0.5: print('(%d, %d, %.3f)' % (n,nbr,data))

10. 图生成和图上的一些操作

下方的这些操作都是在networkx包内的方法

subgraph(G, nbunch)      - induce subgraph of G on nodes in nbunch
union(G1,G2)             - graph union
disjoint_union(G1,G2)    - graph union assuming all nodes are different
cartesian_product(G1,G2) - return Cartesian product graph
compose(G1,G2)           - combine graphs identifying nodes common to both
complement(G)            - graph complement
create_empty_copy(G)     - return an empty copy of the same graph class
convert_to_undirected(G) - return an undirected representation of G
convert_to_directed(G)   - return a directed representation of G

11. 图上分析

g = nx.Graph()
g.add_edges_from([(1,2), (1,3)])
g.add_node("spam") 
nx.connected_components(g) # [[1, 2, 3], ['spam']] 表示返回g上的不同连通块
sorted(nx.degree(g).values())

通过构建权值图，可以直接快速利用dijkstra_path()接口计算最短路程

>>> G=nx.Graph()
>>> e=[('a','b',0.3),('b','c',0.9),('a','c',0.5),('c','d',1.2)]
>>> G.add_weighted_edges_from(e)
>>> print(nx.dijkstra_path(G,'a','d'))
['a', 'c', 'd']

12. 图的绘制

下面是4种图的构造方法，选择其中一个

nx.draw(g)
nx.draw_random(g) #点随机分布
nx.draw_circular(g) #点的分布形成一个环
nx.draw_spectral(g)

最后将图形表现出来

1 2	import matplotlib.pyplot as plt plt.show()

将图片保存到下来

1 2	nx.draw(g) plt.savefig("path.png")

修改节点颜色，边的颜色

1
2
3

g = nx.cubical_graph()
nx.draw(g, pos=nx.spectral_layout(g), nodecolor='r', edge_color='b')
plt.show()

13. 图形种类的选择

Graph Type	NetworkX Class
简单无向图	Graph()
简单有向图	DiGraph()
有自环	Grap(),DiGraph()
有重边	MultiGraph(), MultiDiGraph()

reference：https://networkx.github.io/documentation/networkx-1.10/reference/classes.html

Junnor.G