diff --git a/R/plot_simplified_edgeplot.R b/R/plot_simplified_edgeplot.R
index d851967..d0c55c3 100644
--- a/R/plot_simplified_edgeplot.R
+++ b/R/plot_simplified_edgeplot.R
@@ -5,11 +5,40 @@
 #' @param communities optional; A communities object
 #' @param ... other arguments to be passed to the `plot()` function
 #' @examples
-#' # Create an igraph object
-#' my_graph <- igraph::make_ring(10)
-#' # Plot the simplified edge plot
-#' plot_simplified_edgeplot(my_graph)
+#'# Load the igraph package
+#' library(igraph)
+#' library(ig.degree.betweenness)
+#' # Set parameters
+#' num_nodes <- 17     # Number of nodes (adjust as needed)
+#' initial_edges <- 1   # Starting edges for preferential attachment
 #'
+#' # Create a directed, scale-free network using the Barabási-Albert model
+#' g <- sample_pa(n = num_nodes, m = initial_edges, directed = TRUE)
+#'
+#' # Introduce additional edges to high-degree nodes to accentuate popularity differences
+#' num_extra_edges <- 350   # Additional edges to create more popular nodes
+#' set.seed(123)           # For reproducibility
+#'
+#' for (i in 1:num_extra_edges) {
+#'   # Sample nodes with probability proportional to their degree (to reinforce popularity)
+#'   from <- sample(V(g), 1, prob = degree(g, mode = "in") + 1)  # +1 to avoid zero probabilities
+#'   to <- sample(V(g), 1)
+#'
+#'   # Ensure we don't add the same edge repeatedly unless intended, allowing self-loops
+#'   g <- add_edges(g, c(from, to))
+#' }
+#'
+#' # Add self-loops to a subset of nodes
+#' num_self_loops <- 5
+#' for (i in 1:num_self_loops) {
+#'   node <- sample(V(g), 1)
+#'   g <- add_edges(g, c(node, node))
+#' }
+#'
+#'
+#' g_ <- ig.degree.betweenness::prep_unlabeled_graph(g)
+#'
+#' ig.degree.betweenness::plot_simplified_edgeplot(g_,main="Simulated Data")
 #' @export
 
 plot_simplified_edgeplot <- function(graph,
diff --git a/man/plot_simplified_edgeplot.Rd b/man/plot_simplified_edgeplot.Rd
index fcf3495..56b1d9f 100644
--- a/man/plot_simplified_edgeplot.Rd
+++ b/man/plot_simplified_edgeplot.Rd
@@ -19,9 +19,38 @@ Plot Simplified Edgeplot
 This function generates a simplified edge plot of an igraph object, optionally highlighting communities if provided.
 }
 \examples{
-# Create an igraph object
-my_graph <- igraph::make_ring(10)
-# Plot the simplified edge plot
-plot_simplified_edgeplot(my_graph)
+# Load the igraph package
+library(igraph)
+library(ig.degree.betweenness)
+# Set parameters
+num_nodes <- 17     # Number of nodes (adjust as needed)
+initial_edges <- 1   # Starting edges for preferential attachment
 
+# Create a directed, scale-free network using the Barabási-Albert model
+g <- sample_pa(n = num_nodes, m = initial_edges, directed = TRUE)
+
+# Introduce additional edges to high-degree nodes to accentuate popularity differences
+num_extra_edges <- 350   # Additional edges to create more popular nodes
+set.seed(123)           # For reproducibility
+
+for (i in 1:num_extra_edges) {
+  # Sample nodes with probability proportional to their degree (to reinforce popularity)
+  from <- sample(V(g), 1, prob = degree(g, mode = "in") + 1)  # +1 to avoid zero probabilities
+  to <- sample(V(g), 1)
+
+  # Ensure we don't add the same edge repeatedly unless intended, allowing self-loops
+  g <- add_edges(g, c(from, to))
+}
+
+# Add self-loops to a subset of nodes
+num_self_loops <- 5
+for (i in 1:num_self_loops) {
+  node <- sample(V(g), 1)
+  g <- add_edges(g, c(node, node))
+}
+
+
+g_ <- ig.degree.betweenness::prep_unlabeled_graph(g)
+
+ig.degree.betweenness::plot_simplified_edgeplot(g_,main="Simulated Data")
 }