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

H = nx.path_graph(10)
g.add_nodes_from(H) #將0~9加入了節點
#但請勿使用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)數據類型不對，*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條連續的邊

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

g.number_of_nodes() #查看點的數量
g.number_of_edges() #查看邊的數量
g.nodes() #返回所有點的信息(list)
g.edges() #返回所有邊的信息(list中每個元素是一個tuple)
g.neighbors(1) #所有與1這個點相連的點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false就是不輸出屬性信息，修改為true，會將節點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)]) # 添加帶權值的邊
print DG.out_degree(1) # 打印結果：2 表示：找到1的出度
print DG.out_degree(1, weight='weight') # 打印結果：0.8 表示：從1出去的邊的權值和，這里權值是以weight屬性值作為標準，如果你有一個money屬性，那么也可以修改為weight='money'，那么結果就是對money求和了
print DG.successors(1) # [2,4] 表示1的后繼節點有2和4
print DG.predecessors(1) # [3] 表示只有一個節點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表示每一個起始點，nbrs是一個字典，字典中的每一個元素包含了這個起始點連接的點和這兩個點連邊對應的屬性
 print n, nbrs
 for nbr,eattr in nbrs.items():
  # nbr表示跟n連接的點，eattr表示這兩個點連邊的屬性集合，這里只設置了weight，如果你還設置了color，那么就可以通過eattr['color']訪問到對應的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) #點隨機分布
nx.draw_circular(g) #點的分布形成一個環
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()