R/degree_w.R

In tnet: Weighted, Two-Mode, and Longitudinal Networks Analysis

`degree_w` <-
function(net, measure = c("degree", "output"), type = "out", alpha=1){
  # 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")

  # Reverse data if calculating in-degrees
  if(type == "in") {
    net <- data.frame(i=net[,2], j=net[,1], w=net[,3])
    net <- net[order(net[,"i"],net[,"j"]),]
  }
  # Create an index for each node
  index <- cumsum(!duplicated(net[,1]))
  # Create output object
  k.list <- cbind(unique(net[,1]), NaN, NaN, NaN)
  # Assign names
  dimnames(k.list)[[2]] <- c("node", "degree", "output","alpha")
  # Calculating degree
  if(sum(c("degree","alpha") %in% measure)>0) 
    k.list[,"degree"] <- tapply(net[, "w"], index, length)
  # Calculating strength
  if(sum(c("output","alpha") %in% measure)>0) 
    k.list[,"output"] <- tapply(net[, "w"], index, sum)
  # Calculating alpha parameter
  if("alpha" %in% measure) 
    k.list[,"alpha"] <- k.list[,"degree"]*((k.list[,"output"]/k.list[,"degree"])^alpha)
  # Add rows to the output object if isolates exists
  if(max(net[,c("i", "j")]) != nrow(k.list)) {
    k.list <- rbind(k.list, cbind(1:max(net[, c("i", "j")]), 0, 0, 0))
    k.list <- k.list[order(k.list[, "node"]), ]
    k.list <- k.list[!duplicated(k.list[, "node"]), ]
  }
  # Extract just relevant columns, and return
  return(k.list[,c("node", measure)])
}