R/RelativeNetworkInformation.R

In dils: Data-Informed Link Strength. Combine multiple-relationship networks into a single weighted network. Impute (fill-in) missing network links.

#' Compare how much two networks inform a particular network measure
#'
#' Given two \code{igraph} networks, use \code{\link{MeasureNetworkInformation}}
#' to gauge the informativeness of each network and then return the ratio. If
#' greater than 1, then the first network specified is more informative.
#' 
#' @param g1 igraph, graph to measure
#' @param g2 igraph, graph to measure
#' @param FUN function, a function that takes an igraph and returns a value for each node in the network.
#' @param remove.share numeric, fraction of the edges that are removed randomly when perturbing the network.
#' @param sample.size numeric, number of perturbed graphs to generate
#' @param progress.bar logical, if TRUE then a progress bar is shown.
#' @return list, containing the following
#' \tabular{lll}{
#'   \code{g1.over.g2} \tab numeric \tab informativeness of the first network over the second \cr
#'   \code{winner} \tab character \tab either g1 or g2 \cr
#'   \code{g1.measure} \tab numeric \tab \code{MeasureNetworkInformation(g1, ...)} \cr
#'   \code{g2.measure} \tab numeric \tab \code{MeasureNetworkInformation(g2, ...)} \cr
#' }
#' @export
#' @references
#' \url{https://github.com/shaptonstahl/dils}
#' @author Stephen R. Haptonstahl \email{srh@@haptonstahl.org}
#' @details This measure appears to be very sensitive to the choice of \code{FUN}.
#' See \code{\link{MeasureNetworkInformation}} for details.
#' @examples
#' g.rand <- random.graph.game(100, 5/100)
#' 
#' pf <- matrix( c(.8, .2, .3, .7), nr=2)
#' g.pref <- preference.game(100, 2, pref.matrix=pf)
#' 
#' RelativeNetworkInformation(g.rand, g.pref)
RelativeNetworkInformation <- function(g1,
                                       g2,
                                       FUN=betweenness,
                                       remove.share=.2,
                                       sample.size=100,
                                       progress.bar=FALSE) {
  m1 <- MeasureNetworkInformation(g1,
                                  FUN=FUN,
                                  remove.share=remove.share,
                                  sample.size=sample.size,
                                  progress.bar=progress.bar)
  m2 <- MeasureNetworkInformation(g2,
                                  FUN=FUN,
                                  remove.share=remove.share,
                                  sample.size=sample.size,
                                  progress.bar=progress.bar)
  return(list(g1.over.g2=m1/m2,
              winner=ifelse(m1>m2, "g1", "g2"),
              g1.measure=m1,
              g2.measure=m2))
}