# 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. nodes – Dictionary of nodes with eigenvector centrality as the value. 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 in-edges in the graph. For out-edges eigenvector centrality first reverse the graph with G.reverse().

eigenvector_centrality(), pagerank(), hits()