dev/R_igraph/similarity.R

In kbodwin/longnet: What the Package Does (One Line, Title Case)

#' Similarity measures of two vertices
#'
#' These functions calculates similarity scores for vertices based on their
#' connection patterns.
#'
#' @details
#' The Jaccard similarity coefficient of two vertices is the number of common
#' neighbors divided by the number of vertices that are neighbors of at least
#' one of the two vertices being considered. The \code{jaccard} method
#' calculates the pairwise Jaccard similarities for some (or all) of the
#' vertices.
#'
#' The Dice similarity coefficient of two vertices is twice the number of
#' common neighbors divided by the sum of the degrees of the vertices.
#' Methof \code{dice} calculates the pairwise Dice similarities for some
#' (or all) of the vertices.
#'
#' The inverse log-weighted similarity of two vertices is the number of their
#' common neighbors, weighted by the inverse logarithm of their degrees.  It is
#' based on the assumption that two vertices should be considered more similar
#' if they share a low-degree common neighbor, since high-degree common
#' neighbors are more likely to appear even by pure chance.  Isolated vertices
#' will have zero similarity to any other vertex.  Self-similarities are not
#' calculated.  See the following paper for more details: Lada A. Adamic and
#' Eytan Adar: Friends and neighbors on the Web. Social Networks,
#' 25(3):211-230, 2003.
#'
#' @aliases similarity.jaccard similarity.dice similarity.invlogweighted
#' @param graph The input graph.
#' @param vids The vertex ids for which the similarity is calculated.
#' @param mode The type of neighboring vertices to use for the calculation,
#'   possible values: \sQuote{\code{out}}, \sQuote{\code{in}},
#'   \sQuote{\code{all}}.
#' @param loops Whether to include vertices themselves in the neighbor
#'   sets.
#' @param method The method to use.
#' @return A \code{length(vids)} by \code{length(vids)} numeric matrix
#'   containing the similarity scores. This argument is ignored by the
#'   \code{invlogweighted} method.
#' @author Tamas Nepusz \email{ntamas@@gmail.com} and Gabor Csardi
#'   \email{csardi.gabor@@gmail.com} for the manual page.
#' @seealso \code{\link{cocitation}} and \code{\link{bibcoupling}}
#' @references Lada A. Adamic and Eytan Adar: Friends and neighbors on the Web.
#'   \emph{Social Networks}, 25(3):211-230, 2003.
#' @keywords graphs
#' @export
#' @examples
#'
#' g <- make_ring(5)
#' similarity(g, method = "dice")
#' similarity(g, method = "jaccard")

similarity <- function(graph, vids = V(graph), mode = c("all", "out", "in",
                          "total"), loops = FALSE, method = c("jaccard",
                          "dice", "invlogweighted")) {

  method <- igraph.match.arg(method)
  if (method == "jaccard") {
    similarity.jaccard(graph, vids, mode, loops)
  } else if (method == "dice") {
    similarity.dice(graph, vids, mode, loops)
  } else if (method == "invlogweighted") {
    similarity.invlogweighted(graph, vids, mode)
  }
}