`bonpow()`

was renamed to `power_centrality()`

to create a more
consistent API.

## Usage

```
bonpow(
graph,
nodes = V(graph),
loops = FALSE,
exponent = 1,
rescale = FALSE,
tol = 1e-07,
sparse = TRUE
)
```

## Arguments

- graph
the input graph.

- nodes
vertex sequence indicating which vertices are to be included in the calculation. By default, all vertices are included.

- loops
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops.

`loops`

is`FALSE`

by default.- exponent
exponent (decay rate) for the Bonacich power centrality score; can be negative

- rescale
if true, centrality scores are rescaled such that they sum to 1.

- 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