diff --git a/CHANGELOG.md b/CHANGELOG.md
index 36f855cc0ee4..56fd02033c72 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [2.0.5] - 2022-MM-DD
 ### Added
+- Added a `normalize` parameter to `dense_diff_pool` ([#4847](https://github.com/pyg-team/pytorch_geometric/pull/4847))
 - Added `size=None` explanation to jittable `MessagePassing` modules in the documentation ([#4850](https://github.com/pyg-team/pytorch_geometric/pull/4850))
 - Added documentation to the `DataLoaderIterator` class ([#4838](https://github.com/pyg-team/pytorch_geometric/pull/4838))
 - Added `GraphStore` support to `Data` and `HeteroData` ([#4816](https://github.com/pyg-team/pytorch_geometric/pull/4816))
diff --git a/torch_geometric/nn/dense/diff_pool.py b/torch_geometric/nn/dense/diff_pool.py
index 54e746431325..6e6abb92e0cf 100644
--- a/torch_geometric/nn/dense/diff_pool.py
+++ b/torch_geometric/nn/dense/diff_pool.py
@@ -3,7 +3,7 @@
 EPS = 1e-15
 
 
-def dense_diff_pool(x, adj, s, mask=None):
+def dense_diff_pool(x, adj, s, mask=None, normalize=True):
     r"""The differentiable pooling operator from the `"Hierarchical Graph
     Representation Learning with Differentiable Pooling"
     <https://arxiv.org/abs/1806.08804>`_ paper
@@ -44,6 +44,9 @@ def dense_diff_pool(x, adj, s, mask=None):
         mask (BoolTensor, optional): Mask matrix
             :math:`\mathbf{M} \in {\{ 0, 1 \}}^{B \times N}` indicating
             the valid nodes for each graph. (default: :obj:`None`)
+        normalize (bool, optional): If set to :obj:`False`, the link
+            prediction loss is not divided by :obj:`adj.numel()`.
+            (default: :obj:`True`)
 
     :rtype: (:class:`Tensor`, :class:`Tensor`, :class:`Tensor`,
         :class:`Tensor`)
@@ -66,7 +69,8 @@ def dense_diff_pool(x, adj, s, mask=None):
 
     link_loss = adj - torch.matmul(s, s.transpose(1, 2))
     link_loss = torch.norm(link_loss, p=2)
-    link_loss = link_loss / adj.numel()
+    if normalize is True:
+        link_loss = link_loss / adj.numel()
 
     ent_loss = (-s * torch.log(s + EPS)).sum(dim=-1).mean()