场景：基于python库networkx来求解图最短路径，相关算法基础参考

http://blog.csdn.net/fjssharpsword/article/details/52931373

http://blog.csdn.net/fjssharpsword/article/details/52953640

可看networkx源码了解其内部算法用的原理。

代码：

# -*- coding: utf-8 -*-

import networkx as nx

import matplotlib.pyplot as plt

#读取文件，获取节点和边

f = open("D:\\tmp\\gy_contest_link_top.txt", "r")

nodelist=[]

edgelist=[]

while True:

line = f.readline()

if line:

pass # do something here

line=line.strip()

node=line.split(';')[0]#获取图节点

nodelist.append(node)

in_nodes=line.split(';')[1].split('#')#获取图边，该节点是终点

for ins in range( len(in_nodes) ) :

if in_nodes[ins].strip() !='':

in_edge=(in_nodes[ins],node)

if in_edge not in edgelist:

edgelist.append(in_edge)

out_nodes=line.split(';')[2].split('#')#获取图边，该节点是起点

for ins in range( len(out_nodes) ) :

if out_nodes[ins].strip() !='':

out_edge=(node,out_nodes[ins])

if out_edge not in edgelist:

edgelist.append(out_edge)

else:

break

f.close()

del nodelist[0] #删除表头生成的节点

del edgelist[0]

del edgelist[0] #删除表头生成的边

#print len(nodelist) #图节点

#print len(edgelist) #边数

#有向图构建

G=nx.DiGraph()

G.add_nodes_from(nodelist)

G.add_edges_from(edgelist)

#任意两点间最短路径

'''

try:

n=nx.shortest_path_length(G,1,4)

print n

except nx.NetworkXNoPath:

print 'No path'

'''

path=nx.all_pairs_shortest_path(G)

#for item in path.items():

# print item

print path.popitem()

#强联通和弱联通

for c in nx.weakly_connected_components(G):#弱联通

print c

con = nx.strongly_connected_components(G)#强联通

print con

print type(con)

print list(con)

#有向图绘制

nx.draw_networkx(G, pos=None, arrows=True, with_labels=False)#with_labels=False不带节点名

#plt.savefig('D:\\tmp\\it.png')

plt.show()