44 lines
1.2 KiB
R
44 lines
1.2 KiB
R
newman_mod <- function(g, weights=NULL){
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A <- get.adjacency(g) # adj
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m <- ecount(g)
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n <- vcount(g)
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if (is.null(weights)){
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weights <- rep(1,n)
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}
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# Obtain the modularity matrix
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B.node.i <- function(i){degree(g)[i]*degree(g)}
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B.node.all <- sapply(1:n, B.node.i)
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B <- A - (B.node.all/(2*m))
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# NOTE: This is identical to: modularity_matrix(g) ! Can verify with:
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# modularity_matrix(g) == B
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B.eigs <- eigen(B)
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max.lam <- B.eigs$values[1]
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s <- ifelse(B.eigs$vectors[,1]>0,1,-1)
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weights <- B.eigs$vectors[n]/B.eigs$vectors[,1]
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# Plotting
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#V(g)$color <- ifelse(B[1,]>0,"green","yellow")
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V(g)$color <- ifelse(B.eigs$vectors[,1]>0,"green","yellow")
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V(g)$size <- 10
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plot(g, main=paste(g.netname, " Newman Modularity"))
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clust1 = list()
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clust2 = list()
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clusters = list()
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# Make list of clusters
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for(i in 1:n){
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ifelse(V(g)[i]$color=="green",
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clust1 <- append(clust1, V(g)[i]$name),
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clust2 <- append(clust2, V(g)[i]$name))}
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clusters <- list(clust1, clust2)
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Q.node.i <- function(i){sum(
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(((B.eigs$vectors[i])*weights[i]*s)^2)*B.eigs[i]$values)}
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Q <- (1/(4*m))*sapply(1:n, Q.node.i)
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return(list(Q=Q,max.lam=max.lam,weights=weights,clusters=clusters))
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}
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