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- #!/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)
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