R/local.R

In tidygraph: A Tidy API for Graph Manipulation

#' Measures based on the neighborhood of each node
#'
#' These functions wraps a set of functions that all measures quantities of the
#' local neighborhood of each node. They all return a vector or list matching
#' the node position.
#'
#' @return A numeric vector or a list (for `local_members`) with elements
#' corresponding to the nodes in the graph.
#'
#' @name local_graph
#' @rdname local_graph
#'
#' @examples
#' # Get all neighbors of each graph
#' create_notable('chvatal') %>%
#'   activate(nodes) %>%
#'   mutate(neighborhood = local_members(mindist = 1))
#'
#' # These are equivalent
#' create_notable('chvatal') %>%
#'   activate(nodes) %>%
#'   mutate(n_neighbors = local_size(mindist = 1),
#'          degree = centrality_degree()) %>%
#'   as_tibble()
#'
NULL

#' @describeIn local_graph The size of the neighborhood in a given distance from
#' the node. (Note that the node itself is included unless `mindist > 0`). Wraps [igraph::ego_size()].
#' @inheritParams igraph::ego_size
#' @importFrom igraph ego_size
#' @export
local_size <- function(order = 1, mode = 'all', mindist = 0) {
  expect_nodes()
  graph <- .G()
  ego_size(graph = graph, order = order, nodes = focus_ind(graph, 'nodes'), mode = mode, mindist = mindist)
}
#' @describeIn local_graph The members of the neighborhood of each node in a
#' given distance. Wraps [igraph::ego()].
#' @importFrom igraph ego
#' @export
local_members <- function(order = 1, mode = 'all', mindist = 0) {
  expect_nodes()
  graph <- .G()
  lapply(ego(graph = graph, order = order, nodes = focus_ind(graph, 'nodes'), mode = mode, mindist = mindist), as.integer)
}
#' @describeIn local_graph The number of triangles each node participate in. Wraps [igraph::count_triangles()].
#' @importFrom igraph count_triangles
#' @export
local_triangles <- function() {
  expect_nodes()
  graph <- .G()
  count_triangles(graph = graph, vids = focus_ind(graph, 'nodes'))
}
#' @describeIn local_graph Calculates the average degree based on the neighborhood of each node. Wraps [igraph::knn()].
#' @param weights An edge weight vector. For `local_ave_degree`: If this argument
#' is given, the average vertex strength is calculated instead of vertex degree.
#' For `local_transitivity`: if given weighted transitivity using the approach by
#' *A. Barrat* will be calculated.
#' @importFrom igraph knn
#' @export
local_ave_degree <- function(weights = NULL) {
  expect_nodes()
  weights <- enquo(weights)
  weights <- eval_tidy(weights, .E()) %||% NA
  graph <- .G()
  knn(graph = graph, vids = focus_ind(graph, 'nodes'), weights = weights)$knn
}
#' @describeIn local_graph Calculate the transitivity of each node, that is, the
#' propensity for the nodes neighbors to be connected. Wraps [igraph::transitivity()]
#' @importFrom igraph transitivity V
#' @importFrom rlang quos
#' @export
local_transitivity <- function(weights = NULL) {
  expect_nodes()
  weights <- enquo(weights)
  weights <- eval_tidy(weights, .E())
  type <- if (is.null(weights)) {
    'weighted'
  } else {
    'local'
  }
  graph <- .G()
  transitivity(graph = graph, type = type, vids = focus_ind(graph, 'nodes'), weights = weights, isolates = 'zero')
}