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

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加一組點(diǎn)來說，字符串表示了添加了每一個(gè)字符都代表的多個(gè)節(jié)點(diǎn)，exp：
g.add_node("spam") #添加了一個(gè)名為spam的節(jié)點(diǎn)
g.add_nodes_from("spam") #添加了4個(gè)節(jié)點(diǎn)，名為s,p,a,m
g.nodes() #可以將以上5個(gè)節(jié)點(diǎn)打印出來看看

H = nx.path_graph(10)
g.add_nodes_from(H) #將0~9加入了節(jié)點(diǎn)
#但請(qǐng)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)數(shù)據(jù)類型不對(duì)，*是將元組中的元素取出

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這樣的n-1條連續(xù)的邊

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

g.number_of_nodes() #查看點(diǎn)的數(shù)量
g.number_of_edges() #查看邊的數(shù)量
g.nodes() #返回所有點(diǎn)的信息(list)
g.edges() #返回所有邊的信息(list中每個(gè)元素是一個(gè)tuple)
g.neighbors(1) #所有與1這個(gè)點(diǎn)相連的點(diǎn)的信息以列表的形式返回
g[1] #查看所有與1相連的邊的屬性，格式輸出：{0: {}, 2: {}} 表示1和0相連的邊沒有設(shè)置任何屬性（也就是{}沒有信息），同理1和2相連的邊也沒有任何屬性

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默認(rèn)false就是不輸出屬性信息，修改為true，會(huì)將節(jié)點(diǎn)名字和屬性信息一起輸出

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)]) # 添加帶權(quán)值的邊
print DG.out_degree(1) # 打印結(jié)果：2 表示：找到1的出度
print DG.out_degree(1, weight='weight') # 打印結(jié)果：0.8 表示：從1出去的邊的權(quán)值和，這里權(quán)值是以weight屬性值作為標(biāo)準(zhǔn)，如果你有一個(gè)money屬性，那么也可以修改為weight='money'，那么結(jié)果就是對(duì)money求和了
print DG.successors(1) # [2,4] 表示1的后繼節(jié)點(diǎn)有2和4
print DG.predecessors(1) # [3] 表示只有一個(gè)節(jié)點(diǎn)3有指向1的連邊

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表示每一個(gè)起始點(diǎn)，nbrs是一個(gè)字典，字典中的每一個(gè)元素包含了這個(gè)起始點(diǎn)連接的點(diǎn)和這兩個(gè)點(diǎn)連邊對(duì)應(yīng)的屬性
 print n, nbrs
 for nbr,eattr in nbrs.items():
  # nbr表示跟n連接的點(diǎn)，eattr表示這兩個(gè)點(diǎn)連邊的屬性集合，這里只設(shè)置了weight，如果你還設(shè)置了color，那么就可以通過eattr['color']訪問到對(duì)應(yīng)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上的不同連通塊
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) #點(diǎn)隨機(jī)分布
nx.draw_circular(g) #點(diǎn)的分布形成一個(gè)環(huán)
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()