import networkx as nx
g = nx.Graph()
g.clear() #将图上元素清空

g.add_node(1)
g.add_node("a")
g.add_node("spam")

g.add_nodes_from([2,3])
or 
a = [2,3]
g.add_nodes_from(a)

add_nodes_from加一组点来说，字符串表示了添加了每一个字符都代表的多个节点，exp：
g.add_node("spam") #添加了一个名为spam的节点
g.add_nodes_from("spam") #添加了4个节点，名为s,p,a,m
g.nodes() #可以将以上5个节点打印出来看看

H = nx.path_graph(10)
g.add_nodes_from(H) #将0~9加入了节点
#但请勿使用g.add_node(H)

g.remove_node(node_name)
g.remove_nodes_from(nodes_list)

g.add_edge(1,2)
e = (2,3)
g.add_edge(*e) #直接g.add_edge(e)数据类型不对，*是将元组中的元素取出

g.add_edges_from([(1,2),(1,3)])
g.add_edges_from([("a","spam") , ("a",2)])

n = 10
H = nx.path_graph(n)
g.add_edges_from(H.edges()) #添加了0~1,1~2 ... n-2~n-1这样的n-1条连续的边

g.remove_edge(edge)
g.remove_edges_from(edges_list)

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

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

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

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

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

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

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]

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))

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

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()) 

> 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']

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)

import matplotlib.pyplot as plt
plt.show()

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

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