Nothing
`weighted_richclub_local_w` <-
function(net,prominence){
# Ensure that the network conforms to the tnet standard
if (is.null(attributes(net)$tnet)) net <- as.tnet(net, type = "weighted one-mode tnet")
if (attributes(net)$tnet != "weighted one-mode tnet") stop("Network not loaded properly")
N <- max(c(net[,"i"],net[,"j"]))
if(length(prominence) != N)
stop("The length of the prominence parameter is not equal to the number of nodes")
# Create output table
output <- cbind(node=1:N, degree=NaN, strength=NaN, degree2p=NaN, num=NaN, den=NaN, ratio=NaN)
# Go through every node
for(i in 1:N) {
# Find the number of ties
output[i,"degree"] <- sum(net[,1]==i)
# Find the strength of a node
output[i,"strength"] <- sum(net[net[,1]==i,3])
# Find the number of ties to prominent nodes
output[i,"degree2p"] <- sum(net[,1]==i & net[,2]%in%which(prominence==1))
# Find the sum of weights to prominent nodes
output[i,"num"] <- sum(net[net[,1]==i & net[,2]%in%which(prominence==1),3])
# Close the for-loop for every node
}
# Find the randomly expected value
output[,"den"] <- (output[,"strength"]/output[,"degree"])*output[,"degree2p"]
# Divide the values on each other to get the ratio
output[,"ratio"] <- output[,"num"]/output[,"den"]
# In case no ties to prominent nodes, assign a value of 1
output[output[,"degree2p"]==0,"ratio"] <- 1
# Output
return(output[,c("node","ratio")])
}
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