R/normalizeSimilarity.R

In massimoaria/bibliometrix: Comprehensive Science Mapping Analysis

#' Calculate similarity indices
#'
#' It calculates a relative measure of bibliographic co-occurrences.
#'
#' \code{couplingSimilarity} calculates Association strength, Inclusion, Jaccard or Salton similarity from a co-occurrence bibliographic matrix.
#'
#' The association strength is used by Van Eck and Waltman (2007) and Van Eck et al. (2006). Several works refer to the measure as the proximity index,
#' while Leydesdorff (2008)and Zitt et al. (2000) refer to it as the probabilistic affinity (or activity) index.
#'
#' The inclusion index, also called Simpson coefficient, is an overlap measure used in information retrieval.
#'
#' The Jaccard index (or Jaccard similarity coefficient) gives us a relative measure of the overlap of two sets.
#' It is calculated as the ratio between the intersection and the union of the reference lists (of two manuscripts).
#'
#' The Salton index, instead, relates the intersection of the two lists to the geometric mean of the size of both sets.
#' The square of Salton index is also called Equivalence index.
#'
#' The indices are equal to zero if the intersection of the reference lists is empty.\cr\cr
#'
#' References\cr\cr
#' Leydesdorff, L. (2008). On the normalization and visualization of author Cocitation data: Salton's cosine versus the Jaccard index.
#' Journal of the American Society for Information Science and Technology, 59(1), 77– 85.\cr
#' Van Eck, N.J., Waltman, L., Van den Berg, J., & Kaymak, U. (2006). Visualizing the computational intelligence field.
#' IEEE Computational Intelligence Magazine, 1(4), 6– 10.\cr
#' Van Eck, N.J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field.
#' International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(5), 625– 645\cr.
#' Van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures.
#' Journal of the American society for information science and technology, 60(8), 1635-1651.\cr
#' Zitt, M., Bassecoulard, E., & Okubo, Y. (2000). Shadows of the past in international cooperation:
#' Collaboration profiles of the top five producers of science. Scientometrics, 47(3), 627– 657.\cr
#'
#'
#' @param NetMatrix is a coupling matrix obtained by the network functions \code{\link{biblioNetwork}} or \code{\link{cocMatrix}}.
#' @param type is a character. It can be "association", "jaccard", "inclusion","salton" or "equivalence" to obtain Association Strength, Jaccard,
#' Inclusion, Salton or Equivalence similarity index respectively. The default is \code{type = "association"}.
#' @return a similarity matrix.
#'
#'
#'
#' @examples
#'
#' data(scientometrics, package = "bibliometrixData")
#' NetMatrix <- biblioNetwork(scientometrics,
#'   analysis = "co-occurrences",
#'   network = "keywords", sep = ";"
#' )
#' S <- normalizeSimilarity(NetMatrix, type = "association")
#'
#' @seealso \code{\link{biblioNetwork}} function to compute a bibliographic network.
#' @seealso \code{\link{cocMatrix}} to compute a bibliographic bipartite network.
#'
#' @export

normalizeSimilarity <- function(NetMatrix, type = "association") {
  diag <- Matrix::diag
  D <- diag(NetMatrix)
  # S=NetMatrix
  switch(type,
    association = {
      S <- NetMatrix / ((outer(D, D, "*")))
    },
    inclusion = {
      S <- NetMatrix / outer(D, D, function(a, b) {
        mapply(min, a, b)
      })
    },
    jaccard = {
      S <- NetMatrix / (outer(D, D, "+") - NetMatrix)
    },
    salton = {
      S <- NetMatrix / (sqrt(outer(D, D, "*")))
    },
    equivalence = {
      S <- (NetMatrix / sqrt(outer(D, D, "*")))^2
    }
  )

  S <- as.matrix(S)
  S[is.nan(S)] <- 0
  S <- Matrix(S, sparse = TRUE)
  # if (class(S)!="dgCMatrix"){S=as.matrix(S)}

  return(S)
}