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#' Sample a random dot product graph as an igraph graph
#'
#' @inherit sample_edgelist params details references examples description
#'
#' @return An [igraph::igraph()] object that is possibly a
#' multigraph (that is, we take there to be multiple edges
#' rather than weighted edges).
#'
#' When `factor_model` is **undirected**:
#'
#' - the graph is undirected and one-mode.
#'
#' When `factor_model` is **directed** and **square**:
#'
#' - the graph is directed and one-mode.
#'
#' When `factor_model` is **directed** and **rectangular**:
#'
#' - the graph is undirected and bipartite.
#'
#' Note that working with bipartite graphs in `igraph` is more
#' complex than working with one-mode graphs.
#'
#' @export
#' @family samplers
#'
sample_igraph <- function(
factor_model,
...) {
ellipsis::check_dots_unnamed()
if (!(requireNamespace("igraph", quietly = TRUE))) {
stop(
"Must install `igraph` package to return graphs as `igraph` ",
"objects",
call. = FALSE
)
}
UseMethod("sample_igraph")
}
#' @rdname sample_igraph
#' @export
sample_igraph.undirected_factor_model <- function(
factor_model,
...) {
edgelist <- sample_edgelist(factor_model, ...)
nodes <- tibble(name = 1:nrow(factor_model$X))
igraph::graph_from_data_frame(edgelist, directed = FALSE, vertices = nodes)
}
#' @rdname sample_igraph
#' @export
sample_igraph.directed_factor_model <- function(
factor_model,
...) {
if (factor_model$n == factor_model$d) {
edgelist <- sample_edgelist(factor_model, ...)
nodes <- tibble(name = 1:nrow(factor_model$X))
one_mode <- igraph::graph_from_data_frame(edgelist, directed = TRUE, vertices = nodes)
return(one_mode)
}
# to represent a rectangular adjacency matrix igraph we have to make
# a bipartite graph rather than using the usual one-mode tools
A <- sample_sparse(factor_model, ...)
igraph::graph_from_incidence_matrix(A, directed = FALSE, multiple = TRUE)
}
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