Compute the eigenvector centrality for the graph G.
Parameters : | G : graph
|
---|---|
Returns : | nodes : dictionary
|
See also
eigenvector_centrality, pagerank, hits
Notes
This algorithm uses the NumPy eigenvalue solver.
For directed graphs this is “right” eigevector centrality. For “left” eigenvector centrality, first reverse the graph with G.reverse().
Examples
>>> G=nx.path_graph(4)
>>> centrality=nx.eigenvector_centrality_numpy(G)
>>> print(['%s %0.2f'%(node,centrality[node]) for node in centrality])
['0 0.37', '1 0.60', '2 0.60', '3 0.37']