Hi all,

I have a question about the using the power-centrality function for computation of Bonacich centrality. I would like to construct a scaled power centrality with two 2 inputs, *alpha* and *beta*

From the power-centrality function, I can set the value of *beta*. In my case, I would like to set it to 3/4 of the reciprocal of the maximum eigenvalue of the network. (`exponent`

=0.75/max_eigen)

However, there is a restriction to choose the scaling parameter *alpha*

The `rescale`

option, if true, centrality scores are rescaled such that they sum to 1. I would like to choose the value of *alpha* such that the minimum centrality of a vertex is always equal to 1. Is there a solution to this issue?

Thank you for your help.

**Code example:**

```
mynetwork= graph.data.frame(mydata,directed=FALSE)
ec <- evcent(mynetwork)
ec <- ec$vector # evcent give the unnormalized eigenvector
ed <- eigen(as.matrix(get.adjacency(mynetwork))) # eigen return a normalized vector
ec2 <- ed$vectors[,1]
ec2
max_eigen <- max(ec2)
bonacich <- power_centrality(network,
loops = FALSE, exponent=0.75/max_eigen,
rescale = FALSE,
sparse = TRUE)
```