`evcent()`

was renamed to `eigen_centrality()`

to create a more
consistent API.

## Usage

```
evcent(
graph,
directed = FALSE,
scale = TRUE,
weights = NULL,
options = arpack_defaults()
)
```

## Arguments

- graph
Graph to be analyzed.

- directed
Logical scalar, whether to consider direction of the edges in directed graphs. It is ignored for undirected graphs.

- scale
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.

- weights
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 that is used. If`weights`

is a numerical vector then it is used, even if the graph has a`weight`

edge attribute. If this is`NA`

, then no edge weights are used (even if the graph has a`weight`

edge attribute). Note that if there are negative edge weights and the direction of the edges is considered, then the eigenvector might be complex. In this case only the real part is reported. This function interprets weights as connection strength. Higher weights spread the centrality better.- options
A named list, to override some ARPACK options. See

`arpack()`

for details.