added NetSwan functions

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: netrankr
 Type: Package
 Title: Analyzing Partial Rankings in Networks
-Version: 1.2.3.9000
+Version: 1.2.4
 Authors@R: c(
     person("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2952-4812")),
@@ -25,7 +25,7 @@ Imports: igraph (>= 1.0.1),
     Rcpp (>= 0.12.8),
     Matrix
 LinkingTo: Rcpp,RcppArmadillo
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 Suggests: 
     knitr,

NAMESPACE

@@ -31,6 +31,10 @@ export(neighborhood_inclusion)
 export(positional_dominance)
 export(rank_intervals)
 export(spectral_gap)
+export(swan_closeness)
+export(swan_combinatory)
+export(swan_connectivity)
+export(swan_efficiency)
 export(threshold_graph)
 export(transitive_reduction)
 export(walks_attenuated)

NEWS.md

@@ -1,3 +1,7 @@
+# netrankr 1.2.4
+
+* added functions from archived NetSwan package
+
 # netrankr 1.2.3
 
 * removed test causing issues on some platforms

R/netswan.R

@@ -0,0 +1,293 @@
+#' @name swan_closeness
+#' @title Impact on closeness when a node is removed
+#'
+#' @description
+#' `swan_closeness` measures the change in the sum of the inverse of distances between all node pairs
+#' when excluding that node.#'
+#' @param g An `igraph` object representing the graph to analyze.
+#'
+#' @details
+#' `swan_closeness` measures the impact of a node's removal by computing the change in
+#' the sum of inverse distances between all node pairs.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A numeric vector containing the `swan_closeness` values for all vertices.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un contexte de risques*.
+#' Cybergeo: European Journal of Geography.
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute swan_closeness
+#' f2 <- swan_closeness(gra)
+#'
+#' # Compare with betweenness centrality
+#' bet <- betweenness(gra)
+#' reg <- lm(bet ~ f2)
+#' summary(reg)
+#' @export
+swan_closeness <- function(g) {
+  n <- igraph::vcount(g)
+  swancc <- rep(0, n)
+  cc <- igraph::distances(g)
+  ccb <- 1 / cc
+  ccb[is.infinite(ccb)] <- 0
+  tot <- sum(ccb)
+  for (i in seq_len(n)) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    cc_no_i <- igraph::distances(g2)
+    cc_no_ib <- 1 / cc_no_i
+    cc_no_ib[is.infinite(cc_no_ib)] <- 0
+    tot2 <- sum(cc_no_ib)
+    swancc[i] <- tot2 - (tot - sum(ccb[i, ]) - sum(ccb[, i]))
+  }
+  return(swancc)
+}
+
+#' @name swan_combinatory
+#' @title Error and attack tolerance of complex networks
+#'
+#' @description
+#' `swan_combinatory` assesses network vulnerability and the resistance of networks
+#' to node removals, whether due to random failures or intentional attacks.
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#' @param k The number of iterations for assessing the impact of random failures.
+#'
+#' @details
+#' Many complex systems display a surprising degree of tolerance against random failures.
+#' However, this resilience often comes at the cost of extreme vulnerability to targeted attacks,
+#' where removing key nodes (high-degree or high-betweenness nodes) can severely impact network connectivity.
+#'
+#' `swan_combinatory` simulates different attack strategies:
+#' - **Random failure:** Nodes are removed randomly over multiple iterations.
+#' - **Degree-based attack:** Nodes are removed in decreasing order of their degree.
+#' - **Betweenness-based attack:** Nodes are removed in decreasing order of their betweenness centrality.
+#' - **Cascading failure:** Nodes are removed based on recalculated betweenness after each removal.
+#'
+#' The function returns a matrix showing the connectivity loss for each attack scenario.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A matrix with five columns:
+#' \itemize{
+#'   \item Column 1: Fraction of nodes removed.
+#'   \item Column 2: Connectivity loss from betweenness-based attack.
+#'   \item Column 3: Connectivity loss from degree-based attack.
+#'   \item Column 4: Connectivity loss from cascading failure.
+#'   \item Column 5: Connectivity loss from random failures (averaged over `k` iterations).
+#' }
+#'
+#' @references
+#' Albert R., Jeong H., Barabási A. (2000). *Error and attack tolerance of complex networks*.
+#' Nature, 406(6794), 378-382.
+#'
+#' @examples
+#' library(igraph)
+#' # Example electrical network graph
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute vulnerability measures
+#' f4 <- swan_combinatory(gra, 10)
+#' @export
+swan_combinatory <- function(g, k) {
+  n <- igraph::vcount(g)
+  dist <- igraph::distances(g)
+  dist[is.infinite(dist)] <- 0
+  dist[dist > 0] <- 1
+  tot <- sum(dist)
+  fin <- matrix(ncol = 5, nrow = n, 0)
+  mat <- matrix(ncol = 2, nrow = n, 0)
+  mat[, 1] <- 1:n
+  bet <- igraph::betweenness(g)
+  mat[, 2] <- bet
+  matri <- mat[order(mat[, 2]), ]
+  g2 <- g
+  for (i in seq_len(n)) {
+    v = n + 1 - i
+    g2 <- igraph::delete_vertices(g2, matri[v, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 1] <- i / n
+    fin[i, 2] <- tot - tot2
+    matri[matri[, 1] > matri[v, 1], 1] <- matri[matri[, 1] > matri[v, 1], 1] - 1
+  }
+  mat <- matrix(ncol = 2, nrow = n, 0) #degree attack
+  mat[, 1] <- 1:n
+  deg <- igraph::degree(g)
+  mat[, 2] <- deg
+  matri <- mat[order(mat[, 2]), ]
+  g2 <- g
+  for (i in seq_len(n)) {
+    v = n + 1 - i
+    g2 <- igraph::delete_vertices(g2, matri[v, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 3] <- tot - tot2
+    matri[matri[, 1] > matri[v, 1], 1] <- matri[matri[, 1] > matri[v, 1], 1] -
+      1 #bluff
+  }
+  g2 <- g #cascading
+  npro <- n
+  lim <- n - 1
+  for (i in 1:lim) {
+    mat <- matrix(ncol = 2, nrow = npro, 0)
+    mat[, 1] <- 1:npro
+    bet <- igraph::betweenness(g2)
+    mat[, 2] <- bet
+    matri <- mat[order(mat[, 2]), ]
+    g2 <- igraph::delete_vertices(g2, matri[npro, 1])
+    dist2 <- igraph::distances(g2)
+    dist2[is.infinite(dist2)] <- 0
+    dist2[dist2 > 0] <- 1
+    tot2 <- sum(dist2)
+    fin[i, 4] <- tot - tot2
+    npro <- npro - 1
+  }
+  fin[n, 4] <- tot
+  #random
+  for (l in seq_len(k)) {
+    al <- sample(1:n, n)
+    g2 <- g
+    for (i in seq_len(k)) {
+      g2 <- igraph::delete_vertices(g2, al[i])
+      dist2 <- igraph::distances(g2)
+      dist2[is.infinite(dist2)] <- 0
+      dist2[dist2 > 0] <- 1
+      tot2 <- sum(dist2)
+      fin[i, 5] <- fin[i, 5] + (tot - tot2)
+      al[al > al[i]] <- al[al > al[i]] - 1 #bluff
+    }
+  }
+  fin[, 2:4] <- fin[, 2:4] / tot
+  fin[, 5] <- fin[, 5] / tot / k
+  return(fin)
+}
+
+#' @name swan_connectivity
+#' @title Impact on connectivity when a node is removed
+#'
+#' @description
+#' `swan_connectivity` measures the loss of connectivity when a node is removed from the network.
+#'
+#' @usage
+#' swan_connectivity(g)
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#'
+#' @details
+#' Connectivity loss indices quantify the decrease in the number of relationships between nodes
+#' when one or more components are removed. `swan_connectivity` computes the connectivity loss
+#' by systematically excluding each node and evaluating the resulting changes in the network structure.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#' @return
+#' A numeric vector where each entry represents the connectivity loss when the corresponding node is removed.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un contexte de risques*.
+#' Cybergeo: European Journal of Geography.
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute connectivity loss
+#' f3 <- swan_connectivity(gra)
+#' @export
+swan_connectivity <- function(g) {
+  n <- igraph::vcount(g)
+  fin <- rep(0, n)
+  dist <- igraph::distances(g)
+  dist[!is.infinite(dist)] <- 0
+  dist[is.infinite(dist)] <- 1
+  con <- sum(dist)
+  for (i in seq_len(n)) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    dist2 <- igraph::distances(g2)
+    dist2[!is.infinite(dist2)] <- 0
+    dist2[is.infinite(dist2)] <- 1
+    con2 <- sum(dist2)
+    fin[i] <- con2 - con
+  }
+  return(fin)
+}
+
+#' @name swan_efficiency
+#' @title Impact on farness when a node is removed
+#'
+#' @description
+#' `swan_efficiency` measures the change in the sum of distances between all node pairs
+#' when excluding a node from the network.
+#'
+#' @param g An `igraph` object representing the graph to analyze.
+#' `swan_efficiency` is based on geographic accessibility, similar to indices used for
+#' assessing transportation network performance, such as closeness accessibility.
+#' It quantifies the impact of node removal by calculating the change in the sum of
+#' distances between all node pairs.
+#'
+#' The code is an adaptation from the NetSwan package that was archived on CRAN.
+#'
+#' @return
+#' A numeric vector where each entry represents the `swan_efficiency` value for the
+#' corresponding node.
+#'
+#' @references
+#' Lhomme S. (2015). *Analyse spatiale de la structure des réseaux techniques dans un
+#' contexte de risques*. Cybergeo: European Journal of Geography.
+#'
+#' @examples
+#' library(igraph)
+#' # Example graph (electrical network structure)
+#' elec <- matrix(ncol = 2, byrow = TRUE, c(
+#'   11,1, 11,10, 1,2, 2,3, 2,9,
+#'   3,4, 3,8, 4,5, 5,6, 5,7,
+#'   6,7, 7,8, 8,9, 9,10
+#' ))
+#' gra <- graph_from_edgelist(elec, directed = FALSE)
+#'
+#' # Compute efficiency impact of node removal
+#' f2 <- swan_efficiency(gra)
+#' bet <- betweenness(gra)
+#' reg <- lm(bet ~ f2)
+#' summary(reg)
+#' @export
+swan_efficiency <- function(g) {
+  n <- igraph::vcount(g)
+  fin <- rep(0, n)
+  dt = igraph::distances(g)
+  tot = sum(dt)
+  for (i in 1:n) {
+    g2 <- g
+    g2 <- igraph::delete_vertices(g2, i)
+    dt2 <- igraph::distances(g2)
+    tot2 <- sum(dt2)
+    fin[i] <- tot2 - (tot - sum(dt[i, ]) - sum(dt[, i]))
+  }
+  return(fin)
+}

cran-comments.md

@@ -1,7 +1,6 @@
-# Update from 1.2.2 to 1.2.3
+# Update from 1.2.3 to 1.2.4
 
-quick bugfix release to prevent package from being deleted from CRAN due to
-failing test
+added functions from an archived CRAN package
 
 ## Test environments
 * ubuntu 22.04, R 4.3.2