diff --git a/DESCRIPTION b/DESCRIPTION
index 1734a94..302ac39 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -16,7 +16,7 @@ Authors@R: c(
 Description: This package contains functions that help in manipulating tables and generating plots for multi-omics analysis including genomics, transcriptomics, proteomics, methylomics and immunoinformatics.
 License: CC BY-NC-SA 4.0
 Encoding: UTF-8
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Imports:
     dplyr,
     ggplot2,
@@ -30,7 +30,10 @@ Imports:
     grid,
     utils,
     patchwork,
-    cowplot
+    cowplot,
+    igraph,
+    visNetwork,
+    cluster
 Suggests: 
     biomaRt,
     dbscan,
diff --git a/NAMESPACE b/NAMESPACE
index cd794b9..ed9408b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(add_annotations)
+export(build_net)
 export(detect_filter)
 export(get_annotations)
 export(get_stars)
@@ -17,9 +18,29 @@ export(power_analysis)
 export(save_results)
 export(split_cases)
 export(tpm)
+export(view_net)
 import(ggplot2)
+importFrom(cluster,silhouette)
 importFrom(cowplot,get_legend)
+importFrom(dplyr,distinct)
+importFrom(dplyr,filter)
+importFrom(dplyr,left_join)
+importFrom(dplyr,pull)
+importFrom(dplyr,rename)
+importFrom(igraph,E)
+importFrom(igraph,V)
+importFrom(igraph,as_data_frame)
+importFrom(igraph,graph_from_adjacency_matrix)
 importFrom(magrittr,"%>%")
 importFrom(patchwork,plot_layout)
 importFrom(rlang,.data)
+importFrom(stats,cutree)
+importFrom(stats,dist)
+importFrom(stats,hclust)
+importFrom(tibble,tibble)
 importFrom(utils,modifyList)
+importFrom(utils,read.delim)
+importFrom(visNetwork,visLayout)
+importFrom(visNetwork,visNetwork)
+importFrom(visNetwork,visOptions)
+importFrom(visNetwork,visSave)
diff --git a/R/build_net.R b/R/build_net.R
new file mode 100644
index 0000000..7c6bea5
--- /dev/null
+++ b/R/build_net.R
@@ -0,0 +1,238 @@
+###############################
+# Build enrichment clustering #
+###############################
+
+#' Calculate gene set clusters from GMT files and enrichment results
+#'
+#' This function parses one or more GMT files, filters an enrichment results
+#' data frame by an FDR column, computes a Jaccard similarity matrix between
+#' selected gene sets, performs hierarchical clustering, and determines the
+#' optimal number of clusters using the silhouette index.
+#'
+#' @param results_df A \code{data.frame} or \code{tibble} containing enrichment
+#'   results. Must contain at least the columns specified by \code{fdr_col} and
+#'   \code{pathway_col}.
+#' @param gmt_path A character scalar. Path to a directory containing one or
+#'   more \code{.gmt} files.
+#' @param fdr_threshold A numeric scalar specifying the FDR cutoff used to
+#'   filter \code{results_df}. Only pathways with FDR values less than this
+#'   threshold will be considered. Default is \code{0.25}.
+#' @param fdr_col A character scalar specifying the column name in
+#'   \code{results_df} that contains FDR values.
+#' @param pathway_col A character scalar specifying the column name in
+#'   \code{results_df} that contains pathway identifiers (names matching the
+#'   GMT gene set names).
+#' @param max_k_ratio A numeric scalar used to determine the maximum number of
+#'   clusters to evaluate with the silhouette index. The maximum k is computed
+#'   as \code{max(2, floor(n / max_k_ratio))}, where \code{n} is the number of
+#'   selected gene sets. Default is \code{2.5}.
+#'
+#' @details
+#' GMT files are expected to be tab-delimited, with the first column being the
+#' gene set name, the second column a description (which is ignored), and the
+#' remaining columns listing gene identifiers.
+#'
+#' Jaccard similarity between gene sets \eqn{A} and \eqn{B} is defined as:
+#' \deqn{J(A,B) = |A \cap B| / |A \cup B|}.
+#'
+#' Hierarchical clustering is performed using Euclidean distances derived from
+#' \code{1 - Jaccard_similarity} and the \code{"ward.D2"} linkage method.
+#' The optimal number of clusters is chosen as the \code{k} that maximizes the
+#' mean silhouette width for \code{k} in \code{2:max_k}.
+#'
+#' This function does not perform any file I/O beyond reading GMT files; it is
+#' intended to be used inside packages and downstream plotting functions.
+#'
+#' @return A named list with the following elements:
+#' \itemize{
+#'   \item \code{clusters}: A \code{tibble} with columns \code{pathway} and
+#'     \code{cluster} (integer cluster memberships).
+#'   \item \code{jaccard_matrix}: A symmetric numeric matrix of Jaccard
+#'     similarities between gene sets (rows and columns named by pathways).
+#'   \item \code{hclust_obj}: An \code{\link[stats]{hclust}} object containing
+#'     the hierarchical clustering result.
+#'   \item \code{optimal_k}: An integer giving the selected number of clusters.
+#'   \item \code{silhouette_data}: A \code{tibble} with columns \code{k} and
+#'     \code{silhouette}, suitable for plotting the silhouette index as a
+#'     function of \code{k}.
+#' }
+#'
+#' @examples
+#' \dontrun{
+#' res <- read.csv("pathway_results.csv")
+#' clustering <- build_net(
+#'   results_df   = res,
+#'   gmt_path     = "gmt_dir/",
+#'   fdr_threshold = 0.25,
+#'   fdr_col       = "FDR_gsea",
+#'   pathway_col   = "Pathway",
+#'   max_k_ratio   = 2.5
+#' )
+#' }
+#'
+#' @importFrom stats hclust dist cutree
+#' @importFrom utils read.delim
+#' @importFrom cluster silhouette
+#' @importFrom dplyr filter pull distinct
+#' @importFrom rlang .data
+#' @importFrom tibble tibble
+#' @export
+
+build_net <- function(results_df,
+                                       gmt_path,
+                                       fdr_threshold = 0.25,
+                                       fdr_col,
+                                       pathway_col,
+                                       max_k_ratio = 2.5) {
+  # Basic input checks
+  if (!dir.exists(gmt_path)) {
+    stop("The provided 'gmt_path' does not exist: ", gmt_path)
+  }
+  if (!is.data.frame(results_df)) {
+    stop("'results_df' must be a data.frame or tibble.")
+  }
+  if (!is.character(fdr_col) || length(fdr_col) != 1L) {
+    stop("'fdr_col' must be a single character string.")
+  }
+  if (!is.character(pathway_col) || length(pathway_col) != 1L) {
+    stop("'pathway_col' must be a single character string.")
+  }
+  if (!fdr_col %in% colnames(results_df)) {
+    stop("Column '", fdr_col, "' not found in 'results_df'.")
+  }
+  if (!pathway_col %in% colnames(results_df)) {
+    stop("Column '", pathway_col, "' not found in 'results_df'.")
+  }
+  if (!is.numeric(fdr_threshold) || length(fdr_threshold) != 1L || is.na(fdr_threshold)) {
+    stop("'fdr_threshold' must be a single numeric value.")
+  }
+  if (!is.numeric(max_k_ratio) || length(max_k_ratio) != 1L || is.na(max_k_ratio) || max_k_ratio <= 0) {
+    stop("'max_k_ratio' must be a single positive numeric value.")
+  }
+  
+  # Find GMT files
+  gmt_files <- list.files(gmt_path, pattern = "\\.gmt$", full.names = TRUE)
+  if (length(gmt_files) == 0L) {
+    stop("No .gmt files found in directory: ", gmt_path)
+  }
+  
+  # Read GMT files into a named list of gene vectors
+  geneset_list <- list()
+  for (f in gmt_files) {
+    gmt <- utils::read.delim(f,
+                             header = FALSE,
+                             stringsAsFactors = FALSE,
+                             sep = "\t",
+                             quote = "",
+                             comment.char = "")
+    if (ncol(gmt) < 3L) {
+      next
+    }
+    for (i in seq_len(nrow(gmt))) {
+      name <- as.character(gmt[i, 1])
+      genes <- as.character(gmt[i, 3:ncol(gmt)])
+      genes <- genes[genes != "" & !is.na(genes)]
+      geneset_list[[name]] <- unique(genes)
+    }
+  }
+  
+  if (length(geneset_list) == 0L) {
+    stop("No gene sets could be parsed from the GMT files in '", gmt_path, "'.")
+  }
+  
+  # Filter enrichment results by FDR and select pathways
+  results_tbl <- if (inherits(results_df, "tbl_df")) results_df else tibble::as_tibble(results_df)
+  
+  results_filtered <- dplyr::filter(
+    results_tbl,
+    .data[[fdr_col]] < fdr_threshold
+  )
+  
+  if (nrow(results_filtered) == 0L) {
+    stop("No pathways passed the FDR threshold (", fdr_threshold, ").")
+  }
+  
+  selected_sets <- dplyr::pull(results_filtered, .data[[pathway_col]])
+  selected_sets <- unique(as.character(selected_sets))
+  
+  # Intersect with gene sets available in GMTs
+  selected_sets <- intersect(selected_sets, names(geneset_list))
+  if (length(selected_sets) == 0L) {
+    stop(
+      "No overlap between pathways in 'results_df' and gene set names in GMT files. ",
+      "Check that '", pathway_col, "' matches the GMT gene set names."
+    )
+  }
+  
+  geneset_list <- geneset_list[selected_sets]
+  
+  # Build Jaccard similarity matrix
+  n <- length(geneset_list)
+  jaccard_sim <- matrix(
+    0,
+    nrow = n,
+    ncol  = n,
+    dimnames = list(names(geneset_list), names(geneset_list))
+  )
+  
+  for (i in seq_along(geneset_list)) {
+    for (j in seq_along(geneset_list)) {
+      a <- geneset_list[[i]]
+      b <- geneset_list[[j]]
+      inter <- length(intersect(a, b))
+      union <- length(unique(c(a, b)))
+      if (union == 0) {
+        jaccard_sim[i, j] <- 0
+      } else {
+        jaccard_sim[i, j] <- inter / union
+      }
+    }
+  }
+  
+  # Distance matrix for hierarchical clustering
+  dist_mat <- stats::as.dist(1 - jaccard_sim)
+  
+  # Hierarchical clustering using Ward.D2
+  hc <- stats::hclust(dist_mat, method = "ward.D2")
+  
+  # Determine maximum k for silhouette evaluation
+  max_k <- max(2L, floor(n / max_k_ratio))
+  max_k <- min(max_k, n - 1L) # cannot have k >= n for silhouette
+  
+  if (max_k < 2L) {
+    stop("Not enough gene sets to form at least two clusters.")
+  }
+  
+  ks <- 2:max_k
+  sil_scores <- numeric(length(ks))
+  
+  for (idx in seq_along(ks)) {
+    k <- ks[idx]
+    cl <- stats::cutree(hc, k = k)
+    sil <- cluster::silhouette(cl, dist_mat)
+    sil_scores[idx] <- mean(sil[, "sil_width"])
+  }
+  
+  optimal_k <- ks[which.max(sil_scores)]
+  
+  silhouette_data <- tibble::tibble(
+    k = ks,
+    silhouette = sil_scores
+  )
+  
+  # Cluster membership at optimal k
+  cluster_assignments <- stats::cutree(hc, k = optimal_k)
+  clusters_tbl <- tibble::tibble(
+    pathway = names(cluster_assignments),
+    cluster = as.integer(cluster_assignments)
+  )
+  
+  # Return structured output
+  list(
+    clusters        = clusters_tbl,
+    jaccard_matrix  = jaccard_sim,
+    hclust_obj      = hc,
+    optimal_k       = optimal_k,
+    silhouette_data = silhouette_data
+  )
+}
\ No newline at end of file
diff --git a/R/view_net.R b/R/view_net.R
new file mode 100644
index 0000000..6fdc79c
--- /dev/null
+++ b/R/view_net.R
@@ -0,0 +1,164 @@
+###############################
+# Plot enrichment clustering #
+###############################
+
+#' Plot a gene set network based on clustering output
+#'
+#' This function takes the output of [build_net()],
+#' constructs a gene set similarity network using Jaccard similarity as edge
+#' weights, and generates both a static igraph object and an interactive
+#' \code{visNetwork} visualization.
+#'
+#' @param clustering_output A named list as returned by
+#'    [build_net()], containing at least
+#'   \code{clusters} (a tibble with columns \code{pathway} and \code{cluster})
+#'   and \code{jaccard_matrix} (a symmetric numeric matrix).
+#' @param edge_threshold A numeric scalar specifying the minimum Jaccard
+#'   similarity required for an edge to be drawn between two gene sets.
+#'   All similarities below this threshold are set to zero before network
+#'   construction. Default is \code{0.25}.
+#' @param save_html_path Optional character scalar. If non-\code{NULL}, the
+#'   interactive \code{visNetwork} object will be saved as an HTML file at the
+#'   specified path using \code{visNetwork::visSave}. If \code{NULL}, no HTML
+#'   file is written. Default is \code{NULL}.
+#'
+#' @details
+#' The static graph is built with \pkg{igraph} from a thresholded Jaccard
+#' similarity matrix. Nodes represent gene sets (pathways), and edges represent
+#' Jaccard similarity greater than or equal to \code{edge_threshold}.
+#'
+#' Cluster memberships are taken from \code{clustering_output$clusters} and
+#' mapped to the node attribute \code{group} in the interactive network, which
+#' can be used by \pkg{visNetwork} for coloring.
+#'
+#' This function does not perform any plotting side effects by default; it
+#' returns objects that can be plotted or further customized by the caller.
+#'
+#' @return A named list with the following elements:
+#' \itemize{
+#'   \item \code{static_graph}: An \code{\link[igraph]{igraph}} object built
+#'     from the thresholded Jaccard similarity matrix.
+#'   \item \code{interactive_plot}: A \code{\link[visNetwork]{visNetworkProxy}}
+#'     or \code{visNetwork} object representing the interactive network
+#'     visualization.
+#' }
+#'
+#' @examples
+#' \dontrun{
+#' clustering <- build_net(
+#'   results_df   = res,
+#'   gmt_path     = "gmt_dir/",
+#'   fdr_threshold = 0.25,
+#'   fdr_col       = "FDR_gsea",
+#'   pathway_col   = "Pathway"
+#' )
+#'
+#' plots <- view_net(
+#'   clustering_output = clustering,
+#'   edge_threshold    = 0.25,
+#'   save_html_path    = "network.html"
+#' )
+#'
+#' # Static plot
+#' plot(plots$static_graph,
+#'      vertex.label.cex = 0.7,
+#'      edge.width       = igraph::E(plots$static_graph)$weight * 5)
+#'
+#' # Interactive plot (in RStudio viewer or browser)
+#' plots$interactive_plot
+#' }
+#'
+#' @importFrom igraph graph_from_adjacency_matrix V E as_data_frame
+#' @importFrom visNetwork visNetwork visOptions visLayout visSave
+#' @importFrom dplyr left_join rename
+#' @importFrom tibble tibble
+#' @export
+view_net <- function(clustering_output,
+                                 edge_threshold = 0.25,
+                                 save_html_path = NULL) {
+  # Basic input checks
+  if (!is.list(clustering_output)) {
+    stop("'clustering_output' must be a list as returned by 'calculate_geneset_clusters'.")
+  }
+  required_elements <- c("clusters", "jaccard_matrix")
+  missing_elements <- setdiff(required_elements, names(clustering_output))
+  if (length(missing_elements) > 0L) {
+    stop(
+      "'clustering_output' is missing required elements: ",
+      paste(missing_elements, collapse = ", ")
+    )
+  }
+  
+  clusters_tbl <- clustering_output$clusters
+  jaccard_matrix <- clustering_output$jaccard_matrix
+  
+  if (!is.matrix(jaccard_matrix) || !is.numeric(jaccard_matrix)) {
+    stop("'clustering_output$jaccard_matrix' must be a numeric matrix.")
+  }
+  if (is.null(rownames(jaccard_matrix)) || is.null(colnames(jaccard_matrix))) {
+    stop("The Jaccard matrix must have row and column names corresponding to pathways.")
+  }
+  if (!is.data.frame(clusters_tbl)) {
+    stop("'clustering_output$clusters' must be a data.frame or tibble.")
+  }
+  if (!all(c("pathway", "cluster") %in% colnames(clusters_tbl))) {
+    stop("'clusters' must contain 'pathway' and 'cluster' columns.")
+  }
+  if (!is.numeric(edge_threshold) || length(edge_threshold) != 1L || is.na(edge_threshold)) {
+    stop("'edge_threshold' must be a single numeric value.")
+  }
+  
+  # Threshold the Jaccard matrix to define edges
+  adj <- jaccard_matrix
+  adj[adj < edge_threshold] <- 0
+  diag(adj) <- 0
+  
+  # Build igraph object
+  g <- igraph::graph_from_adjacency_matrix(
+    adjmatrix = adj,
+    mode      = "undirected",
+    weighted  = TRUE,
+    diag      = FALSE
+  )
+  
+  # Static graph (igraph object)
+  static_graph <- g
+  
+  # Prepare nodes and edges for visNetwork
+  pathways <- rownames(adj)
+  
+  nodes <- tibble::tibble(
+    id    = pathways,
+    label = pathways
+  )
+  
+  # Merge cluster membership
+  clusters_tbl <- dplyr::distinct(clusters_tbl, .data$pathway, .data$cluster)
+  nodes <- dplyr::left_join(
+    nodes,
+    dplyr::rename(clusters_tbl, id = .data$pathway, group = .data$cluster),
+    by = "id"
+  )
+  
+  # Convert edges to data.frame
+  edges_df <- igraph::as_data_frame(g, what = "edges")
+  # visNetwork uses columns 'from' and 'to'
+  colnames(edges_df)[1:2] <- c("from", "to")
+  
+  interactive_plot <- visNetwork::visNetwork(nodes, edges_df) |>
+    visNetwork::visOptions(highlightNearest = TRUE) |>
+    visNetwork::visLayout(randomSeed = 174)
+  
+  # Save HTML if requested
+  if (!is.null(save_html_path)) {
+    if (!is.character(save_html_path) || length(save_html_path) != 1L) {
+      stop("'save_html_path' must be a single character string if provided.")
+    }
+    visNetwork::visSave(interactive_plot, file = save_html_path)
+  }
+  
+  list(
+    static_graph     = static_graph,
+    interactive_plot = interactive_plot
+  )
+}
\ No newline at end of file
diff --git a/data/raw_counts.rda b/data/raw_counts.rda
index ea791d6..082f445 100644
Binary files a/data/raw_counts.rda and b/data/raw_counts.rda differ
diff --git a/data/sampledata.rda b/data/sampledata.rda
index 6e956c6..d7d03fc 100644
Binary files a/data/sampledata.rda and b/data/sampledata.rda differ
diff --git a/man/build_net.Rd b/man/build_net.Rd
new file mode 100644
index 0000000..096eb7e
--- /dev/null
+++ b/man/build_net.Rd
@@ -0,0 +1,90 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/build_net.R
+\name{build_net}
+\alias{build_net}
+\title{Calculate gene set clusters from GMT files and enrichment results}
+\usage{
+build_net(
+  results_df,
+  gmt_path,
+  fdr_threshold = 0.25,
+  fdr_col,
+  pathway_col,
+  max_k_ratio = 2.5
+)
+}
+\arguments{
+\item{results_df}{A \code{data.frame} or \code{tibble} containing enrichment
+results. Must contain at least the columns specified by \code{fdr_col} and
+\code{pathway_col}.}
+
+\item{gmt_path}{A character scalar. Path to a directory containing one or
+more \code{.gmt} files.}
+
+\item{fdr_threshold}{A numeric scalar specifying the FDR cutoff used to
+filter \code{results_df}. Only pathways with FDR values less than this
+threshold will be considered. Default is \code{0.25}.}
+
+\item{fdr_col}{A character scalar specifying the column name in
+\code{results_df} that contains FDR values.}
+
+\item{pathway_col}{A character scalar specifying the column name in
+\code{results_df} that contains pathway identifiers (names matching the
+GMT gene set names).}
+
+\item{max_k_ratio}{A numeric scalar used to determine the maximum number of
+clusters to evaluate with the silhouette index. The maximum k is computed
+as \code{max(2, floor(n / max_k_ratio))}, where \code{n} is the number of
+selected gene sets. Default is \code{2.5}.}
+}
+\value{
+A named list with the following elements:
+\itemize{
+\item \code{clusters}: A \code{tibble} with columns \code{pathway} and
+\code{cluster} (integer cluster memberships).
+\item \code{jaccard_matrix}: A symmetric numeric matrix of Jaccard
+similarities between gene sets (rows and columns named by pathways).
+\item \code{hclust_obj}: An \code{\link[stats]{hclust}} object containing
+the hierarchical clustering result.
+\item \code{optimal_k}: An integer giving the selected number of clusters.
+\item \code{silhouette_data}: A \code{tibble} with columns \code{k} and
+\code{silhouette}, suitable for plotting the silhouette index as a
+function of \code{k}.
+}
+}
+\description{
+This function parses one or more GMT files, filters an enrichment results
+data frame by an FDR column, computes a Jaccard similarity matrix between
+selected gene sets, performs hierarchical clustering, and determines the
+optimal number of clusters using the silhouette index.
+}
+\details{
+GMT files are expected to be tab-delimited, with the first column being the
+gene set name, the second column a description (which is ignored), and the
+remaining columns listing gene identifiers.
+
+Jaccard similarity between gene sets \eqn{A} and \eqn{B} is defined as:
+\deqn{J(A,B) = |A \cap B| / |A \cup B|}.
+
+Hierarchical clustering is performed using Euclidean distances derived from
+\code{1 - Jaccard_similarity} and the \code{"ward.D2"} linkage method.
+The optimal number of clusters is chosen as the \code{k} that maximizes the
+mean silhouette width for \code{k} in \code{2:max_k}.
+
+This function does not perform any file I/O beyond reading GMT files; it is
+intended to be used inside packages and downstream plotting functions.
+}
+\examples{
+\dontrun{
+res <- read.csv("pathway_results.csv")
+clustering <- build_net(
+  results_df   = res,
+  gmt_path     = "gmt_dir/",
+  fdr_threshold = 0.25,
+  fdr_col       = "FDR_gsea",
+  pathway_col   = "Pathway",
+  max_k_ratio   = 2.5
+)
+}
+
+}
diff --git a/man/view_net.Rd b/man/view_net.Rd
new file mode 100644
index 0000000..b405b76
--- /dev/null
+++ b/man/view_net.Rd
@@ -0,0 +1,78 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/view_net.R
+\name{view_net}
+\alias{view_net}
+\title{Plot a gene set network based on clustering output}
+\usage{
+view_net(clustering_output, edge_threshold = 0.25, save_html_path = NULL)
+}
+\arguments{
+\item{clustering_output}{A named list as returned by
+\code{\link[=build_net]{build_net()}}, containing at least
+\code{clusters} (a tibble with columns \code{pathway} and \code{cluster})
+and \code{jaccard_matrix} (a symmetric numeric matrix).}
+
+\item{edge_threshold}{A numeric scalar specifying the minimum Jaccard
+similarity required for an edge to be drawn between two gene sets.
+All similarities below this threshold are set to zero before network
+construction. Default is \code{0.25}.}
+
+\item{save_html_path}{Optional character scalar. If non-\code{NULL}, the
+interactive \code{visNetwork} object will be saved as an HTML file at the
+specified path using \code{visNetwork::visSave}. If \code{NULL}, no HTML
+file is written. Default is \code{NULL}.}
+}
+\value{
+A named list with the following elements:
+\itemize{
+\item \code{static_graph}: An \code{\link[igraph]{igraph}} object built
+from the thresholded Jaccard similarity matrix.
+\item \code{interactive_plot}: A \code{\link[visNetwork]{visNetworkProxy}}
+or \code{visNetwork} object representing the interactive network
+visualization.
+}
+}
+\description{
+This function takes the output of \code{\link[=build_net]{build_net()}},
+constructs a gene set similarity network using Jaccard similarity as edge
+weights, and generates both a static igraph object and an interactive
+\code{visNetwork} visualization.
+}
+\details{
+The static graph is built with \pkg{igraph} from a thresholded Jaccard
+similarity matrix. Nodes represent gene sets (pathways), and edges represent
+Jaccard similarity greater than or equal to \code{edge_threshold}.
+
+Cluster memberships are taken from \code{clustering_output$clusters} and
+mapped to the node attribute \code{group} in the interactive network, which
+can be used by \pkg{visNetwork} for coloring.
+
+This function does not perform any plotting side effects by default; it
+returns objects that can be plotted or further customized by the caller.
+}
+\examples{
+\dontrun{
+clustering <- build_net(
+  results_df   = res,
+  gmt_path     = "gmt_dir/",
+  fdr_threshold = 0.25,
+  fdr_col       = "FDR_gsea",
+  pathway_col   = "Pathway"
+)
+
+plots <- view_net(
+  clustering_output = clustering,
+  edge_threshold    = 0.25,
+  save_html_path    = "network.html"
+)
+
+# Static plot
+plot(plots$static_graph,
+     vertex.label.cex = 0.7,
+     edge.width       = igraph::E(plots$static_graph)$weight * 5)
+
+# Interactive plot (in RStudio viewer or browser)
+plots$interactive_plot
+}
+
+}