diff --git a/R/hrg.R b/R/hrg.R
index 3a3fbfb5a9f..81dcd6dd3f8 100644
--- a/R/hrg.R
+++ b/R/hrg.R
@@ -303,6 +303,14 @@ fit_hrg <- function(graph, hrg = NULL, start = FALSE, steps = 0) {
 #'     }
 #'   }
 #' @family hierarchical random graph functions
+#' @examples
+#' ## A graph with two dense groups
+#' g <- sample_gnp(10, p = 1 / 2) + sample_gnp(10, p = 1 / 2)
+#' hrg <- fit_hrg(g)
+#' hrg
+#'
+#' ## The consensus tree for it
+#' consensus_tree(g, hrg = hrg, start = TRUE)
 #' @export
 #' @cdocs igraph_hrg_consensus
 consensus_tree <- function(
diff --git a/man/consensus_tree.Rd b/man/consensus_tree.Rd
index 7025d691b63..b6473b2e3ab 100644
--- a/man/consensus_tree.Rd
+++ b/man/consensus_tree.Rd
@@ -46,6 +46,15 @@ argument is given and \code{start} is set to \code{TRUE}, then it starts
 sampling from the given HRG. Otherwise it optimizes the HRG log-likelihood
 first, and then samples starting from the optimum.
 }
+\examples{
+## A graph with two dense groups
+g <- sample_gnp(10, p = 1 / 2) + sample_gnp(10, p = 1 / 2)
+hrg <- fit_hrg(g)
+hrg
+
+## The consensus tree for it
+consensus_tree(g, hrg = hrg, start = TRUE)
+}
 \seealso{
 Other hierarchical random graph functions: 
 \code{\link{fit_hrg}()},