Merge branch 'main' (early part) into next

Author: krlmlr

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: igraph
 Title: Network Analysis and Visualization
-Version: 2.2.1.9002
+Version: 2.2.1.9003
 Authors@R: c(
     person("Gábor", "Csárdi", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0001-7098-9676")),

NAMESPACE

@@ -222,6 +222,7 @@ export(centralize)
 export(centralize.scores)
 export(chordal_ring)
 export(chung_lu)
+export(circulant)
 export(cit_cit_types)
 export(cit_types)
 export(cited.type.game)
@@ -272,6 +273,7 @@ export(count.multiple)
 export(count_automorphisms)
 export(count_components)
 export(count_isomorphisms)
+export(count_loops)
 export(count_max_cliques)
 export(count_motifs)
 export(count_multiple)
@@ -367,6 +369,7 @@ export(from_prufer)
 export(full_bipartite_graph)
 export(full_citation_graph)
 export(full_graph)
+export(full_multipartite)
 export(get.adjacency)
 export(get.adjedgelist)
 export(get.adjlist)
@@ -650,6 +653,7 @@ export(local_scan)
 export(make_)
 export(make_bipartite_graph)
 export(make_chordal_ring)
+export(make_circulant)
 export(make_clusters)
 export(make_de_bruijn_graph)
 export(make_directed_graph)
@@ -659,6 +663,7 @@ export(make_from_prufer)
 export(make_full_bipartite_graph)
 export(make_full_citation_graph)
 export(make_full_graph)
+export(make_full_multipartite)
 export(make_graph)
 export(make_kautz_graph)
 export(make_lattice)
@@ -667,6 +672,7 @@ export(make_neighborhood_graph)
 export(make_ring)
 export(make_star)
 export(make_tree)
+export(make_turan)
 export(make_undirected_graph)
 export(match_vertices)
 export(max_bipartite_match)
@@ -683,6 +689,7 @@ export(maximal.independent.vertex.sets)
 export(maximal_ivs)
 export(maximum.bipartite.matching)
 export(maximum.cardinality.search)
+export(mean_degree)
 export(mean_distance)
 export(membership)
 export(merge_coords)
@@ -872,6 +879,7 @@ export(tree)
 export(triad.census)
 export(triad_census)
 export(triangles)
+export(turan)
 export(undirected_graph)
 export(unfold.tree)
 export(unfold_tree)

R/aaa-auto.R

@@ -763,6 +763,12 @@ full_multipartite_impl <- function(
     mode
   )
 
+  if (igraph_opt("add.params")) {
+    res$name <- 'Full multipartite graph'
+    res$n <- n
+    res$mode <- mode
+  }
+
   res
 }
 
@@ -853,6 +859,11 @@ circulant_impl <- function(
     directed
   )
 
+  if (igraph_opt("add.params")) {
+    res$name <- 'Circulant graph'
+    res$shifts <- shifts
+  }
+
   res
 }
 
@@ -891,6 +902,12 @@ turan_impl <- function(
     r
   )
 
+  if (igraph_opt("add.params")) {
+    res$name <- 'Turan graph'
+    res$n <- n
+    res$r <- r
+  }
+
   res
 }
 
@@ -6909,6 +6926,7 @@ write_graph_gml_impl <- function(
 ) {
   # Argument checks
   ensure_igraph(graph)
+  options <- switch_igraph_arg(options, "default" = 0L, "encode_only_quot" = 1L)
   id <- as.numeric(id)
 
   on.exit(.Call(R_igraph_finalizer))

R/centrality.R

@@ -2063,7 +2063,7 @@ alpha.centrality.dense <- function(
     attr <- NULL
   } else if (is.character(weights) && length(weights) == 1) {
     ## name of an edge attribute, nothing to do
-    attr <- "weight"
+    attr <- weights
   } else if (any(!is.na(weights))) {
     ## weights != NULL and weights != rep(NA, x)
     graph <- set_edge_attr(graph, "weight", value = as.numeric(weights))
@@ -2110,7 +2110,7 @@ alpha.centrality.sparse <- function(
     attr <- NULL
   } else if (is.character(weights) && length(weights) == 1) {
     ## name of an edge attribute, nothing to do
-    attr <- "weight"
+    attr <- weights
   } else if (any(!is.na(weights))) {
     ## weights != NULL and weights != rep(NA, x)
     graph <- set_edge_attr(graph, "weight", value = as.numeric(weights))

R/make.R

@@ -2286,6 +2286,41 @@ chordal_ring <- function(...) constructor_spec(make_chordal_ring, ...)
 
 ## -----------------------------------------------------------------
 
+#' Create a circulant graph
+#'
+#' A circulant graph \eqn{C_n^{\textrm{shifts}}} consists of \eqn{n} vertices
+#' \eqn{v_0, \ldots, v_{n-1}} such that for each \eqn{s_i} in the list of offsets
+#' `shifts`, \eqn{v_j} is connected to \eqn{v_{(j + s_i) \mod n}} for all \eqn{j}.
+#'
+#' The function can generate either directed or undirected graphs.
+#' It does not generate multi-edges or self-loops.
+#'
+#' @param n Integer, the number of vertices in the circulant graph.
+#' @param shifts Integer vector, a list of the offsets within the circulant graph.
+#' @param directed Boolean, whether to create a directed graph.
+#' @return An igraph graph.
+#'
+#' @family deterministic constructors
+#' @export
+#' @examples
+#' # Create a circulant graph with 10 vertices and shifts 1 and 3
+#' g <- make_circulant(10, c(1, 3))
+#' plot(g, layout = layout_in_circle)
+#'
+#' # A directed circulant graph
+#' g2 <- make_circulant(10, c(1, 3), directed = TRUE)
+#' plot(g2, layout = layout_in_circle)
+make_circulant <- function(n, shifts, directed = FALSE) {
+  circulant_impl(n = n, shifts = shifts, directed = directed)
+}
+
+#' @rdname make_circulant
+#' @param ... Passed to `make_circulant()`.
+#' @export
+circulant <- function(...) constructor_spec(make_circulant, ...)
+
+## -----------------------------------------------------------------
+
 #' Line graph of a graph
 #'
 #' This function calculates the line graph of another graph.
@@ -2582,6 +2617,117 @@ bipartite_graph <- function(...) constructor_spec(make_bipartite_graph, ...)
 
 ## -----------------------------------------------------------------
 
+#' Create a full multipartite graph
+#'
+#' A multipartite graph contains multiple types of vertices and connections
+#' are only possible between vertices of different types. This function
+#' creates a complete multipartite graph where all possible edges between
+#' different partitions are present.
+#'
+#' @param n A numeric vector giving the number of vertices in each partition.
+#' @param directed Logical scalar, whether to create a directed graph.
+#' @param mode Character scalar, the type of connections for directed graphs.
+#'   If `"out"`, then edges point from vertices of partitions with lower
+#'   indices to partitions with higher indices; if `"in"`, then the opposite
+#'   direction is realized; `"all"` creates mutual edges. This parameter is
+#'   ignored for undirected graphs.
+#' @return An igraph graph with a vertex attribute `type` storing the
+#'   partition index of each vertex. Partition indices start from 1.
+#'
+#' @family deterministic constructors
+#' @export
+#' @examples
+#' # Create a multipartite graph with partitions of size 2, 3, and 4
+#' g <- make_full_multipartite(c(2, 3, 4))
+#' plot(g)
+#'
+#' # Create a directed multipartite graph
+#' g2 <- make_full_multipartite(c(2, 2, 2), directed = TRUE, mode = "out")
+#' plot(g2)
+#' @cdocs igraph_full_multipartite
+make_full_multipartite <- function(
+  n,
+  directed = FALSE,
+  mode = c("all", "out", "in")
+) {
+  n <- as.numeric(n)
+  directed <- as.logical(directed)
+  mode <- igraph.match.arg(mode)
+
+  res <- full_multipartite_impl(n = n, directed = directed, mode = mode)
+  graph <- set_vertex_attr(res$graph, "type", value = res$types)
+
+  # Transfer graph attributes from res to graph if add.params is enabled
+  if (igraph_opt("add.params")) {
+    for (attr_name in setdiff(names(res), c("graph", "types"))) {
+      graph <- set_graph_attr(graph, attr_name, res[[attr_name]])
+    }
+  }
+  graph
+}
+
+#' @rdname make_full_multipartite
+#' @param ... Passed to `make_full_multipartite()`.
+#' @export
+full_multipartite <- function(...) {
+  constructor_spec(make_full_multipartite, ...)
+}
+
+## -----------------------------------------------------------------
+
+#' Create a Turán graph
+#'
+#' Turán graphs are complete multipartite graphs with the property that the
+#' sizes of the partitions are as close to equal as possible.
+#'
+#' @details
+#' The Turán graph with `n` vertices and `r` partitions is the densest
+#' graph on `n` vertices that does not contain a clique of size `r+1`.
+#'
+#' This function generates undirected graphs. The null graph is
+#' returned when the number of vertices is zero. A complete graph is
+#' returned if the number of partitions is greater than the number of vertices.
+#'
+#' @param n Integer, the number of vertices in the graph.
+#' @param r Integer, the number of partitions in the graph, must be positive.
+#' @return An igraph graph with a vertex attribute `type` storing the
+#'   partition index of each vertex. Partition indices start from 1.
+#'
+#' @family deterministic constructors
+#' @export
+#' @examples
+#' # Create a Turán graph with 10 vertices and 3 partitions
+#' g <- make_turan(10, 3)
+#' plot(g)
+#'
+#' # The sizes of the partitions are as balanced as possible
+#' table(V(g)$type)
+#' @cdocs igraph_turan
+make_turan <- function(n, r) {
+  n <- as.numeric(n)
+  r <- as.numeric(r)
+
+  res <- turan_impl(n = n, r = r)
+  graph <- set_vertex_attr(res$graph, "type", value = res$types)
+
+  # Transfer graph attributes from res to graph if add.params is enabled
+  if (igraph_opt("add.params")) {
+    for (attr_name in setdiff(names(res), c("graph", "types"))) {
+      graph <- set_graph_attr(graph, attr_name, res[[attr_name]])
+    }
+  }
+  graph
+}
+
+#' @rdname make_turan
+#' @param ... Passed to `make_turan()`.
+#' @export
+turan <- function(...) {
+  constructor_spec(make_turan, ...)
+}
+
+## -----------------------------------------------------------------
+
 #' Create a complete (full) citation graph
 #'
 #' `make_full_citation_graph()` creates a full citation graph. This is a

R/operators.R

@@ -967,7 +967,21 @@ edges <- edge
 #' g
 #' plot(g)
 vertex <- function(...) {
-  structure(list(...), class = "igraph.vertex")
+  args <- list(...)
+  arg_names <- names(args)
+
+  # Check for duplicate named arguments
+  if (!is.null(arg_names)) {
+    named_args <- arg_names[arg_names != ""]
+    if (anyDuplicated(named_args)) {
+      duplicates <- unique(named_args[duplicated(named_args)])
+      cli::cli_abort(
+        "Duplicate attribute {cli::qty(duplicates)}name{?s} in {.fn vertices}: {.val {duplicates}}."
+      )
+    }
+  }
+
+  structure(args, class = "igraph.vertex")
 }
 
 #' @export

R/structural-properties.R

@@ -896,6 +896,10 @@ mean_distance <- function(
 #'   For `max_degree()`, the largest degree in the graph. When no vertices are
 #'   selected, or when the input is the null graph, zero is returned as this
 #'   is the smallest possible degree.
+#'
+#'   For `mean_degree()`, the average degree in the graph as a single number.
+#'   For graphs with no vertices, `NaN` is returned.
+#'   `r lifecycle::badge("experimental")`
 #' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
 #' @keywords graphs
 #' @family structural.properties
@@ -906,6 +910,7 @@ mean_distance <- function(
 #' degree(g)
 #' g2 <- sample_gnp(1000, 10 / 1000)
 #' max_degree(g2)
+#' mean_degree(g2)
 #' degree_distribution(g2)
 #'
 degree <- function(
@@ -981,6 +986,16 @@ max_degree <- function(
     loops = loops
   )
 }
+#' @rdname degree
+#' @export
+#' @cdocs igraph_mean_degree
+mean_degree <- function(graph, loops = TRUE) {
+  mean_degree_impl(
+    graph = graph,
+    loops = loops
+  )
+}
+
 #' @rdname degree
 #' @param cumulative Logical; whether the cumulative degree distribution is to
 #'   be calculated.
@@ -2490,6 +2505,8 @@ girth <- function(graph, circle = TRUE) {
 #'
 #' `which_loop()` decides whether the edges of the graph are loop edges.
 #'
+#' `count_loops()` counts the total number of loop edges in the graph.
+#'
 #' `any_multiple()` decides whether the graph has any multiple edges.
 #'
 #' `which_multiple()` decides whether the edges of the graph are multiple
@@ -2511,6 +2528,7 @@ girth <- function(graph, circle = TRUE) {
 #'   all edges in the graph.
 #' @return `any_loop()` and `any_multiple()` return a logical scalar.
 #'   `which_loop()` and `which_multiple()` return a logical vector.
+#'   `count_loops()` returns a numeric scalar with the total number of loop edges.
 #'   `count_multiple()` returns a numeric vector.
 #' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
 #' @seealso [simplify()] to eliminate loop and multiple edges.
@@ -2523,6 +2541,7 @@ girth <- function(graph, circle = TRUE) {
 #' g <- make_graph(c(1, 1, 2, 2, 3, 3, 4, 5))
 #' any_loop(g)
 #' which_loop(g)
+#' count_loops(g)
 #'
 #' # Multiple edges
 #' g <- sample_pa(10, m = 3, algorithm = "bag")
@@ -2584,6 +2603,14 @@ any_loop <- function(graph) {
     graph = graph
   )
 }
+#' @rdname which_multiple
+#' @export
+#' @cdocs igraph_count_loops
+count_loops <- function(graph) {
+  count_loops_impl(
+    graph = graph
+  )
+}
 
 
 #' Breadth-first search