Find Eigenvector Centrality Scores of Network Positions

evcent takes a graph (graph) and returns the eigenvector centralities of positions v within it

evcent (graph, scale = TRUE, weights = NULL, options = igraph.arpack.default)
Graph to be analyzed.
Logical scalar, whether to scale the result to have a maximum score of one. If no scaling is used then the result vector has unit length in the Euclidean norm.
A numerical vector or NULL. This argument can be used to give edge weights for calculating the weighted eigenvector centrality of vertices. If this is NULL and the graph has a weight edge attribute then t
A named list, to override some ARPACK options. See arpack for details.

Eigenvector centrality scores correspond to the values of the first eigenvector of the graph adjacency matrix; these scores may, in turn, be interpreted as arising from a reciprocal process in which the centrality of each actor is proportional to the sum of the centralities of those actors to whom he or she is connected. In general, vertices with high eigenvector centralities are those which are connected to many other vertices which are, in turn, connected to many others (and so on). (The perceptive may realize that this implies that the largest values will be obtained by individuals in large cliques (or high-density substructures). This is also intelligible from an algebraic point of view, with the first eigenvector being closely related to the best rank-1 approximation of the adjacency matrix (a relationship which is easy to see in the special case of a diagonalizable symmetric real matrix via the $SLS^-1$ decomposition).)

From igraph version 0.5 this function uses ARPACK for the underlying computation, see arpack for more about ARPACK in igraph.


  • A named list with components:
  • vectorA vector containing the centrality scores.
  • valueThe eigenvalue corresponding to the calculated eigenvector, i.e. the centrality scores.
  • optionsA named list, information about the underlying ARPACK computation. See arpack for the details.


Eigenvector centrality


evcent will not symmetrize your data before extracting eigenvectors; don't send this routine asymmetric matrices unless you really mean to do so.


Bonacich, P. (1987). Power and Centrality: A Family of Measures. American Journal of Sociology, 92, 1170-1182.

Katz, L. (1953). A New Status Index Derived from Sociometric Analysis. Psychometrika, 18, 39-43.

  • evcent
#Generate some test data
g <- graph.ring(10, directed=FALSE)
#Compute eigenvector centrality scores
Documentation reproduced from package igraph, version 0.5.5-3, License: GPL (>= 2)

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