diff --git a/CHANGELOG.md b/CHANGELOG.md
index 39e04dd4c985..520931e2417e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -120,7 +120,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a bug in `TUDataset` where `pre_filter` was not applied whenever `pre_transform` was present
 - Renamed `RandomTranslate` to `RandomJitter` - the usage of `RandomTranslate` is now deprecated ([#4828](https://github.com/pyg-team/pytorch_geometric/pull/4828))
 - Do not allow accessing edge types in `HeteroData` with two node types when there exists multiple relations between these types ([#4782](https://github.com/pyg-team/pytorch_geometric/pull/4782))
-- Allow `edge_type == rev_edge_type` argument in `RandomLinkSplit` ([#4757](https://github.com/pyg-team/pytorch_geometric/pull/4757))
+- Allow `edge_type == rev_edge_type` argument in `RandomLinkSplit` ([#4757](https://github.com/pyg-team/pytorch_geometric/pull/4757), [#5221](https://github.com/pyg-team/pytorch_geometric/pull/5221))
 - Fixed a numerical instability in the `GeneralConv` and `neighbor_sample` tests ([#4754](https://github.com/pyg-team/pytorch_geometric/pull/4754))
 - Fixed a bug in `HANConv` in which destination node features rather than source node features were propagated ([#4753](https://github.com/pyg-team/pytorch_geometric/pull/4753))
 - Fixed versions of `checkout` and `setup-python` in CI ([#4751](https://github.com/pyg-team/pytorch_geometric/pull/4751))
diff --git a/test/transforms/test_random_link_split.py b/test/transforms/test_random_link_split.py
index 032abe081af3..b4b049ac3c88 100644
--- a/test/transforms/test_random_link_split.py
+++ b/test/transforms/test_random_link_split.py
@@ -191,3 +191,8 @@ def test_random_link_split_on_undirected_hetero_data():
                                 rev_edge_types=('p', 'p'))
     train_data, val_data, test_data = transform(data)
     assert train_data['p', 'p'].is_undirected()
+
+    transform = RandomLinkSplit(is_undirected=True, edge_types=('p', 'p'),
+                                rev_edge_types=('p', 'p'))
+    train_data, val_data, test_data = transform(data)
+    assert train_data['p', 'p'].is_undirected()