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eigenvector_centrality(G, max_iter=100, tol=1e-06, nstart=None, 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
  • max_iter (integer, optional) – Maximum number of iterations in power method.
  • tol (float, optional) – Error tolerance used to check convergence in power method iteration.
  • nstart (dictionary, optional) – Starting value of eigenvector iteration for each node.
  • weight (None or string, optional) – If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight.

nodes – Dictionary of nodes with eigenvector centrality as the value.

Return type:



>>> G = nx.path_graph(4)
>>> centrality = nx.eigenvector_centrality(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_numpy(), pagerank(), hits(), Notes(), ------(), The(), The(), no(), iterations(), reached.(), For(), to(), first()


[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.