calculate centrality measures for a network using networkx

centrality.py

#!/usr/bin/env python
import networkx as nx

# for each node:
# + degree centrality
# + closeness centrality
# + betweenness centrality
# + eigenvector centrality
# + page rank

def centralissimo(G):
  print 'oh.'

  centralities = []
  centralities.append(nx.degree_centrality(G));      print 'degree centrality: check.'
  centralities.append(nx.closeness_centrality(G));   print 'closeness centrality: check.'
  centralities.append(nx.betweenness_centrality(G)); print 'betweenness centrality: check.'
  centralities.append(nx.eigenvector_centrality(G)); print 'eigenvector centrality: check.'
  centralities.append(nx.pagerank(G));               print 'page rank: check.'

  for node in G.nodes_iter():
    measures = ("\t").join(map(lambda f: str(f[node]), centralities))
    print "%s: %s" % (node, measures)



if __name__ == '__main__':
  import os
  G = nx.read_edgelist(os.path.abspath(os.path.dirname(__file__)) + '/graph.txt', comments='#', delimiter='\t')  
  centralissimo(G)