feat: Add mean_degree() to R (#2415)

Co-authored-by: copilot-swe-agent[bot] <[email protected]>
Co-authored-by: krlmlr <[email protected]>
Co-authored-by: Kirill Müller <[email protected]>

Author: Copilot

NAMESPACE

@@ -683,6 +683,7 @@ 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)

R/structural-properties.R

@@ -894,6 +894,10 @@ 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
@@ -904,6 +908,7 @@ mean_distance <- function(
 #' degree(g)
 #' g2 <- sample_gnp(1000, 10 / 1000)
 #' max_degree(g2)
+#' mean_degree(g2)
 #' degree_distribution(g2)
 #'
 degree <- function(
@@ -951,6 +956,16 @@ max_degree <- function(
   )
 }
 
+#' @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.

man/degree.Rd

@@ -72,6 +72,34 @@ test_that("max_degree() works", {
   expect_equal(max_degree(make_empty_graph()), 0)
 })
 
+test_that("mean_degree() works", {
+  # Undirected graph: formula is 2 * edges / vertices
+  g_undirected <- make_ring(10)
+  expect_equal(mean_degree(g_undirected), 2)
+
+  # Directed graph: formula is edges / vertices
+  # This graph has edges 1->2, 2->2 (loop), 2->3, so 3 edges / 3 vertices = 1
+  g_directed <- make_graph(c(1, 2, 2, 2, 2, 3), directed = TRUE)
+  expect_equal(mean_degree(g_directed), 1)
+
+  # Test loops parameter: excluding the self-loop (2->2)
+  # Without loops: (3 edges - 1 loop) / 3 vertices = 2/3
+  expect_equal(mean_degree(g_directed, loops = FALSE), 2 / 3)
+
+  # Empty graph
+  expect_true(is.nan(mean_degree(make_empty_graph())))
+
+  # Compare with manual calculation for undirected graph
+  g_random <- sample_gnp(100, 0.05)
+  manual_mean <- 2 * ecount(g_random) / vcount(g_random)
+  expect_equal(mean_degree(g_random), manual_mean)
+
+  # For directed graph
+  g_directed_random <- sample_gnp(100, 0.05, directed = TRUE)
+  manual_mean_dir <- ecount(g_directed_random) / vcount(g_directed_random)
+  expect_equal(mean_degree(g_directed_random), manual_mean_dir)
+})
+
 test_that("BFS uses 1-based root vertex index", {
   g <- make_ring(3)
   expect_equal(bfs(g, root = 1)$root, 1)