diff --git a/code/analysis/investigating/impact_of_vem_max_steps.R b/code/analysis/investigating/impact_of_vem_max_steps.R
index 0cc1d23..50f9529 100644
--- a/code/analysis/investigating/impact_of_vem_max_steps.R
+++ b/code/analysis/investigating/impact_of_vem_max_steps.R
@@ -129,7 +129,7 @@ results <- parallel::mclapply(seq_len(nrow(conditions)), function(idx) {
 
     elapsed_time <- stop_time - start_time
 
-    unlisted_best_partition <- extract_best_bipartite_partition(clust)
+    unlisted_best_partition <- extract_best_partition(clust)
 
     if (!is.list(unlisted_best_partition)) {
         unlisted_best_partition <- list(unlisted_best_partition)
diff --git a/code/analysis/investigating/profiling_clustering.R b/code/analysis/investigating/profiling_clustering.R
index 1ebbb79..34ae374 100644
--- a/code/analysis/investigating/profiling_clustering.R
+++ b/code/analysis/investigating/profiling_clustering.R
@@ -121,7 +121,7 @@ results <- parallel::mclapply(seq_len(nrow(conditions)), function(idx) {
 
     elapsed_time <- stop_time - start_time
 
-    unlisted_best_partition <- extract_best_bipartite_partition(clust)
+    unlisted_best_partition <- extract_best_partition(clust)
 
     if (!is.list(unlisted_best_partition)) {
         unlisted_best_partition <- list(unlisted_best_partition)
diff --git a/code/applications/base_analysis.qmd b/code/applications/base_analysis.qmd
index 2014212..b33f61b 100644
--- a/code/applications/base_analysis.qmd
+++ b/code/applications/base_analysis.qmd
@@ -16,7 +16,7 @@ library(latex2exp)
 ```
 ```{r}
 list_collection <- readRDS("{{clustering}}")
-unlisted_best_partition <- unlist(extract_best_bipartite_partition(list_collection))
+unlisted_best_partition <- unlist( extract_best_partition(list_collection))
 
 BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
 ```
diff --git a/code/applications/dore/02_dore_analysis.qmd b/code/applications/dore/02_dore_analysis.qmd
index 56f08fb..8515dd1 100644
--- a/code/applications/dore/02_dore_analysis.qmd
+++ b/code/applications/dore/02_dore_analysis.qmd
@@ -40,7 +40,7 @@ Les clustering donne le critère suivant :
 vec_bicl <- sapply(list_clustering, function(clustering) {
     list_collection <- readRDS(clustering)
     unlisted_best_partition <- unlist(
-        extract_best_bipartite_partition(list_collection)
+         extract_best_partition(list_collection)
     )
     BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
     BICL
diff --git a/code/applications/dore_no_small/02_dore_no_small_analysis.qmd b/code/applications/dore_no_small/02_dore_no_small_analysis.qmd
index 0e89c77..1b006a3 100644
--- a/code/applications/dore_no_small/02_dore_no_small_analysis.qmd
+++ b/code/applications/dore_no_small/02_dore_no_small_analysis.qmd
@@ -121,7 +121,7 @@ Les clustering donne le critère suivant :
 vec_bicl <- sapply(list_clustering, function(clustering) {
     list_collection <- readRDS(clustering)
     unlisted_best_partition <- unlist(
-        extract_best_bipartite_partition(list_collection)
+         extract_best_partition(list_collection)
     )
     BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
     BICL
diff --git a/code/applications/herbivores/03_herbivores_analysis.qmd b/code/applications/herbivores/03_herbivores_analysis.qmd
index 76c81c4..a3da28e 100644
--- a/code/applications/herbivores/03_herbivores_analysis.qmd
+++ b/code/applications/herbivores/03_herbivores_analysis.qmd
@@ -31,7 +31,7 @@ Les clustering donne le critère suivant :
 vec_bicl <- sapply(list_clustering, function(clustering) {
     list_collection <- readRDS(clustering)
     unlisted_best_partition <- unlist(
-        extract_best_bipartite_partition(list_collection)
+         extract_best_partition(list_collection)
     )
     BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
     BICL
diff --git a/code/applications/utils.R b/code/applications/utils.R
index effb1ba..0f9c701 100644
--- a/code/applications/utils.R
+++ b/code/applications/utils.R
@@ -50,7 +50,7 @@ build_graph_size_dataframe <- function(collection_list) {
 }
 
 extract_clustering <- function(clustering) {
-    partition <- colSBM::extract_best_bipartite_partition(
+    partition <- colSBM::extract_best_partition(
         l = clustering,
         unnest = TRUE
     )
diff --git a/code/simulations/clustering/02_synthetic_clustering_analysis.qmd b/code/simulations/clustering/02_synthetic_clustering_analysis.qmd
index ff34cd3..ddffb81 100644
--- a/code/simulations/clustering/02_synthetic_clustering_analysis.qmd
+++ b/code/simulations/clustering/02_synthetic_clustering_analysis.qmd
@@ -41,7 +41,7 @@ Les clustering donne le critère suivant :
 vec_bicl <- sapply(list_clustering, function(clustering) {
     list_collection <- readRDS(clustering)
     unlisted_best_partition <- unlist(
-        extract_best_bipartite_partition(list_collection)
+         extract_best_partition(list_collection)
     )
     BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
     BICL
diff --git a/code/simulations/simulations_network_clustering.R b/code/simulations/simulations_network_clustering.R
index 12b297c..8ffe0fc 100644
--- a/code/simulations/simulations_network_clustering.R
+++ b/code/simulations/simulations_network_clustering.R
@@ -198,7 +198,7 @@ results <- bettermc::mclapply(seq_len(nrow(conditions)), function(s) {
         silent_parallelization = TRUE
     )
 
-    best_partitions <- unlist(extract_best_bipartite_partition(list_collection))
+    best_partitions <- unlist( extract_best_partition(list_collection))
     if (!is(best_partitions, "list")) {
         best_partitions <- list(best_partitions)
     }