See `centralize()`

for a summary of graph centralization.

## Arguments

- graph
The input graph. It can also be

`NULL`

, if`nodes`

is given.- nodes
The number of vertices. This is ignored if the graph is given.

- directed
logical scalar, whether to consider edge directions during the calculation. Ignored in undirected graphs.

- scale
Whether to rescale the eigenvector centrality scores, such that the maximum score is one.

## Value

Real scalar, the theoretical maximum (unnormalized) graph eigenvector centrality score for graphs with given vertex count and other parameters.

## See also

Other centralization related:
`centr_betw()`

,
`centr_betw_tmax()`

,
`centr_clo()`

,
`centr_clo_tmax()`

,
`centr_degree()`

,
`centr_degree_tmax()`

,
`centr_eigen()`

,
`centralize()`

## Examples

```
# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_eigen(g, normalized = FALSE)$centralization %>%
`/`(centr_eigen_tmax(g))
#> [1] 0.9386488
centr_eigen(g, normalized = TRUE)$centralization
#> [1] 0.9386488
```