eigenvector_centrality_numpy¶

eigenvector_centrality_numpy
(G, weight='weight')[source]¶ Compute the eigenvector centrality for the graph G.
Parameters:  G (graph) – A networkx graph
 weight (None or string, optional) – The name of the edge attribute used as weight. If None, all edge weights are considered equal.
Returns: nodes – Dictionary of nodes with eigenvector centrality as the value.
Return type: dictionary
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']
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 inedges in the graph. For outedges eigenvector centrality first reverse the graph with G.reverse().
See also
eigenvector_centrality()
,pagerank()
,hits()