# R igraph manual pages

Use this if you are using igraph from R

## Centralize a graph according to the eigenvector centrality of vertices

### Description

See centralize for a summary of graph centralization.

### Usage

centr_eigen(
graph,
directed = FALSE,
scale = TRUE,
options = arpack_defaults,
normalized = TRUE
)


### Arguments

 graph The input graph. directed logical scalar, whether to use directed shortest paths for calculating eigenvector centrality. scale Whether to rescale the eigenvector centrality scores, such that the maximum score is one. options This is passed to eigen_centrality, the options for the ARPACK eigensolver. normalized Logical scalar. Whether to normalize the graph level centrality score by dividing by the theoretical maximum.

### Value

A named list with the following components:

 vector The node-level centrality scores. value The corresponding eigenvalue. options ARPACK options, see the return value of eigen_centrality for details. centralization The graph level centrality index. theoretical_max The same as above, the theoretical maximum centralization score for a graph with the same number of vertices.

Other centralization related: centr_betw_tmax(), centr_betw(), centr_clo_tmax(), centr_clo(), centr_degree_tmax(), centr_degree(), centr_eigen_tmax(), centralize()

### Examples

# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_degree(g)$centralization centr_clo(g, mode = "all")$centralization
centr_betw(g, directed = FALSE)$centralization centr_eigen(g, directed = FALSE)$centralization

# The most centralized graph according to eigenvector centrality
g0 <- make_graph(c(2,1), n = 10, dir = FALSE)
g1 <- make_star(10, mode = "undirected")
centr_eigen(g0)$centralization centr_eigen(g1)$centralization


[Package igraph version 1.2.7 Index]