forked from noah/Network-Theory_Subgraph-Centrality
54 lines
1.3 KiB
Plaintext
54 lines
1.3 KiB
Plaintext
katz.cent <- function(A, alpha=NULL, beta=NULL){ #NULL sets the default value
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g <- A
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if (class(A) == 'igraph'){
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#Error checking. Turn into adj matrix.
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A <- get.adjacency(A)
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}
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lam.dom <- eigen(A)$values[1] #dom eigenvec
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if (is.null(alpha)){
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alpha <- 0.9 * (1/lam.dom) #Set alpha to 90% of max allowed
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}
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n <- nrow(A)
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if (is.null(beta)){
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beta <- matrix(rep(1/n, n),ncol=1)
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}
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#Katz scores
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scores <- solve(diag(n) - alpha*A,beta)
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names(scores) <- V(g)$name
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return(scores)
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}
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sg.katz <- function(A, alpha=NULL, beta=NULL){
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g <- A
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if (class(A) == 'igraph'){
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# Error checking. Turn into adj matrix if needed.
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A <- get.adjacency(A)
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}
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lam.dom <- eigen(A)$values[1] #dom eigenvec
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if (is.null(alpha)){
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alpha <- 0.9 * (1/lam.dom) #Set alpha to 90% of max allowed
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}
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A.eigs <- eigen(A)
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V <- A.eigs$vectors # where columns are the v_i terms
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lams <- A.eigs$values
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n <- length(lams)
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# Create subfunction to compute centrality for one node, then use sapply
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# for all nodes
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subg.node.i <- function(i){sum(V[i,]^2/(1-alpha*lams))}
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subg.all <- sapply(1:n, subg.node.i)
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# Add names to output
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names(subg.all) <- V(g)$name
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return(subg.all)
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} |