Compute the eigenvector centrality for the graph G.
Parameters: | G : graph
weight : None or string, optional
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Returns: | nodes : dictionary
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See also
eigenvector_centrality, pagerank, hits
Notes
This algorithm uses the SciPy sparse eigenvalue solver (ARPACK) to find the largest eigenvalue/eigenvector pair.
For directed graphs this is “left” eigevector centrality which corresponds to the in-edges in the graph. For out-edges 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']