feat: retire attr in favor of weights for adjacency matrix functions

R/centrality.R

@@ -1760,11 +1760,12 @@ bonpow.dense <- function(
   loops = FALSE,
   exponent = 1,
   rescale = FALSE,
-  tol = 1e-7
+  tol = 1e-7,
+  weights = NULL
 ) {
   ensure_igraph(graph)
 
-  d <- as_adjacency_matrix(graph)
+  d <- as_adjacency_matrix(graph, weights = weights, sparse = FALSE)
   if (!loops) {
     diag(d) <- 0
   }
@@ -1788,7 +1789,8 @@ bonpow.sparse <- function(
   loops = FALSE,
   exponent = 1,
   rescale = FALSE,
-  tol = 1e-07
+  tol = 1e-07,
+  weights = NULL
 ) {
   ## remove loops if requested
   if (!loops) {
@@ -1798,13 +1800,13 @@ bonpow.sparse <- function(
   vg <- vcount(graph)
 
   ## sparse adjacency matrix
-  d <- as_adjacency_matrix(graph, sparse = TRUE)
+  d <- as_adjacency_matrix(graph, weights = weights, sparse = TRUE)
 
   ## sparse identity matrix
   id <- as(Matrix::Matrix(diag(vg), doDiag = FALSE), "generalMatrix")
 
   ## solve it
-  ev <- Matrix::solve(id - exponent * d, degree(graph, mode = "out"), tol = tol)
+  ev <- Matrix::solve(id - exponent * d, Matrix::rowSums(d), tol = tol)
 
   if (rescale) {
     ev <- ev / sum(ev)
@@ -1884,6 +1886,7 @@ bonpow.sparse <- function(
 #' @param sparse Logical scalar, whether to use sparse matrices for the
 #'   calculation. The \sQuote{Matrix} package is required for sparse matrix
 #'   support
+#' @inheritParams as_adjacency_matrix
 #' @return A vector, containing the centrality scores.
 #' @note This function was ported (i.e. copied) from the SNA package.
 #' @section Warning : Singular adjacency matrices cause no end of headaches for
@@ -1936,13 +1939,30 @@ power_centrality <- function(
   exponent = 1,
   rescale = FALSE,
   tol = 1e-7,
-  sparse = TRUE
+  sparse = TRUE,
+  weights = NULL
 ) {
   nodes <- as_igraph_vs(graph, nodes)
   if (sparse) {
-    res <- bonpow.sparse(graph, nodes, loops, exponent, rescale, tol)
+    res <- bonpow.sparse(
+      graph,
+      nodes,
+      loops,
+      exponent,
+      rescale,
+      tol,
+      weights = weights
+    )
   } else {
-    res <- bonpow.dense(graph, nodes, loops, exponent, rescale, tol)
+    res <- bonpow.dense(
+      graph,
+      nodes,
+      loops,
+      exponent,
+      rescale,
+      tol,
+      weights = weights
+    )
   }
 
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
@@ -1966,25 +1986,7 @@ alpha.centrality.dense <- function(
   exo <- rep(exo, length.out = vcount(graph))
   exo <- matrix(exo, ncol = 1)
 
-  if (is.null(weights) && "weight" %in% edge_attr_names(graph)) {
-    ## weights == NULL and there is a "weight" edge attribute
-    attr <- "weight"
-  } else if (is.null(weights)) {
-    ## weights == NULL, but there is no "weight" edge attribute
-    attr <- NULL
-  } else if (is.character(weights) && length(weights) == 1) {
-    ## name of an edge attribute, nothing to do
-    attr <- weights
-  } else if (!all(is.na(weights))) {
-    ## weights != NULL and weights != rep(NA, x)
-    graph <- set_edge_attr(graph, "weight", value = as.numeric(weights))
-    attr <- "weight"
-  } else {
-    ## weights != NULL, but weights == rep(NA, x)
-    attr <- NULL
-  }
-
-  d <- t(as_adjacency_matrix(graph, attr = attr, sparse = FALSE))
+  d <- t(as_adjacency_matrix(graph, weights = weights, sparse = FALSE))
   if (!loops) {
     diag(d) <- 0
   }
@@ -2013,25 +2015,7 @@ alpha.centrality.sparse <- function(
     graph <- simplify(graph, remove.multiple = FALSE, remove.loops = TRUE)
   }
 
-  if (is.null(weights) && "weight" %in% edge_attr_names(graph)) {
-    ## weights == NULL and there is a "weight" edge attribute
-    attr <- "weight"
-  } else if (is.null(weights)) {
-    ## weights == NULL, but there is no "weight" edge attribute
-    attr <- NULL
-  } else if (is.character(weights) && length(weights) == 1) {
-    ## name of an edge attribute, nothing to do
-    attr <- weights
-  } else if (!all(is.na(weights))) {
-    ## weights != NULL and weights != rep(NA, x)
-    graph <- set_edge_attr(graph, "weight", value = as.numeric(weights))
-    attr <- "weight"
-  } else {
-    ## weights != NULL, but weights == rep(NA, x)
-    attr <- NULL
-  }
-
-  M <- Matrix::t(as_adjacency_matrix(graph, attr = attr, sparse = TRUE))
+  M <- Matrix::t(as_adjacency_matrix(graph, weights = weights, sparse = TRUE))
 
   ## Create an identity matrix
   M2 <- Matrix::sparseMatrix(
@@ -2082,11 +2066,7 @@ alpha.centrality.sparse <- function(
 #'   the same factor for every node, or a vector giving the factor for every
 #'   vertex. Note that too long vectors will be truncated and too short vectors
 #'   will be replicated to match the number of vertices.
-#' @param weights A character scalar that gives the name of the edge attribute
-#'   to use in the adjacency matrix. If it is `NULL`, then the
-#'   \sQuote{weight} edge attribute of the graph is used, if there is one.
-#'   Otherwise, or if it is `NA`, then the calculation uses the standard
-#'   adjacency matrix.
+#' @inheritParams as_adjacency_matrix
 #' @param tol Tolerance for near-singularities during matrix inversion, see
 #'   [solve()].
 #' @param sparse Logical scalar, whether to use sparse matrices for the