Networking Example from http://blog.ouseful.info/2012/02/03/journalist-filters-on-twitter-the-reuters-view/

networking_cloud_example

import simplejson
import networkx as nx
#http://mlg.ucd.ie/files/summer/tutorial.pdf
from networkx.algorithms import bipartite

g = nx.Graph()

#need to bring in reutersJournalistList
users=simplejson.loads(reutersJournalistList)

#I had some 'issues' with the parsing for some reason? Required this hack in the end...
for user in users:
	for x in user:
		if x=='users':
			u=user[x][0]['twitter_screen_name']
			print 'user:',user[x][0]['twitter_screen_name']
			for topic in user[x][0]['topics']:
				print '- topic:',topic
				#Add edges from journalist name to each tag they are associated with
				g.add_edge(u,topic)
#print bipartite.is_bipartite(g)
#print bipartite.sets(g)

#Save a graph file we can visualise in Gephi corresponding to bipartite graph
nx.write_graphml(g, "usertags.graphml")

#We can find the sets of names/tags associated with the disjoint sets in the graph
users,tags=bipartite.sets(g)

#Collapse the bipartite graph to a graph of journalists connected via a common tag
ugraph= bipartite.projected_graph(g, users)
nx.write_graphml(ugraph, "users.graphml")

#Collapse the bipartite graph to a set of tags connected via a common journalist
tgraph= bipartite.projected_graph(g, tags)
nx.write_graphml(tgraph, "tags.graphml")

#Dump a list of the journalists Twitter IDs
f=open("users.txt","w+")
for uo in users: f.write(uo+'\n')
f.close()