# Find Bonacich alpha centrality scores of network positions

Source:`R/centrality.R`

`alpha.centrality.Rd`

`alpha.centrality()`

was renamed to `alpha_centrality()`

to create a more
consistent API.

## Usage

```
alpha.centrality(
graph,
nodes = V(graph),
alpha = 1,
loops = FALSE,
exo = 1,
weights = NULL,
tol = 1e-07,
sparse = TRUE
)
```

## Arguments

- graph
The input graph, can be directed or undirected. In undirected graphs, edges are treated as if they were reciprocal directed ones.

- nodes
Vertex sequence, the vertices for which the alpha centrality values are returned. (For technical reasons they will be calculated for all vertices, anyway.)

- alpha
Parameter specifying the relative importance of endogenous versus exogenous factors in the determination of centrality. See details below.

- loops
Whether to eliminate loop edges from the graph before the calculation.

- exo
The exogenous factors, in most cases this is either a constant – the same factor for every node, or a vector giving the factor for every vertex. Note that too long vectors will be truncated and too short vectors will be replicated to match the number of vertices.

- weights
A character scalar that gives the name of the edge attribute to use in the adjacency matrix. If it is

`NULL`

, then the ‘weight’ edge attribute of the graph is used, if there is one. Otherwise, or if it is`NA`

, then the calculation uses the standard adjacency matrix.- tol
Tolerance for near-singularities during matrix inversion, see

`solve()`

.- sparse
Logical scalar, whether to use sparse matrices for the calculation. The ‘Matrix’ package is required for sparse matrix support