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. (
However, there is a restriction to choose the scaling parameter alpha
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.
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)