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# eigenvector_centrality_numpy¶

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]).

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']


eigenvector_centrality(), pagerank(), hits()

Notes

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().

References

 [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.