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eigenvector_centrality_numpy(G, weight='weight')[source]

Compute the eigenvector centrality for the graph G.

Eigenvector centrality computes the centrality for a node based on the centrality of its neighbors. The eigenvector centrality for node \(i\) is

\[\mathbf{Ax} = \lambda \mathbf{x}\]

where \(A\) is the adjacency matrix of the graph G with eigenvalue \(\lambda\). By virtue of the Perron–Frobenius theorem, there is a unique and positive solution if \(\lambda\) is the largest eigenvalue associated with the eigenvector of the adjacency matrix \(A\) ([2]).

  • 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.

Return type:



>>> 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']

See also

eigenvector_centrality(), pagerank(), hits()


The measure was introduced by [1].

This algorithm uses the SciPy sparse eigenvalue solver (ARPACK) to find the largest eigenvalue/eigenvector pair.

For directed graphs this is “left” eigenvector centrality which corresponds to the in-edges in the graph. For out-edges eigenvector centrality first reverse the graph with G.reverse().


[1]Phillip Bonacich: Power and Centrality: A Family of Measures. American Journal of Sociology 92(5):1170–1182, 1986 http://www.leonidzhukov.net/hse/2014/socialnetworks/papers/Bonacich-Centrality.pdf
[2]Mark E. J. Newman: Networks: An Introduction. Oxford University Press, USA, 2010, pp. 169.