# centr_eigen

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

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:

The node-level centrality scores.

The corresponding eigenvalue.

ARPACK options, see the return value of
`eigen_centrality`

for details.

The graph level centrality index.

The same as above, the theoretical maximum centralization score for a graph with the same number of vertices.

##### See Also

Other centralization related:
`centr_betw_tmax()`

,
`centr_betw()`

,
`centr_clo_tmax()`

,
`centr_clo()`

,
`centr_degree_tmax()`

,
`centr_degree()`

,
`centr_eigen_tmax()`

,
`centralize()`

##### Examples

```
# NOT RUN {
# 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
# }
```

*Documentation reproduced from package igraph, version 1.2.5, License: GPL (>= 2)*