diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0cedaf7923b0..fd17cdfe9b16 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added a `batch_size` argument to `unbatch` functionalities ([#7851](https://github.com/pyg-team/pytorch_geometric/pull/7851))
 - Added a distributed example using `graphlearn-for-pytorch` ([#7402](https://github.com/pyg-team/pytorch_geometric/pull/7402))
 - Integrate `neg_sampling_ratio` into `TemporalDataLoader` ([#7644](https://github.com/pyg-team/pytorch_geometric/pull/7644))
 - Added `faiss`-based `KNNINdex` classes for L2 or maximum inner product search ([#7842](https://github.com/pyg-team/pytorch_geometric/pull/7842))
diff --git a/torch_geometric/utils/unbatch.py b/torch_geometric/utils/unbatch.py
index 62efcbf07bb0..0ba85c7e1135 100644
--- a/torch_geometric/utils/unbatch.py
+++ b/torch_geometric/utils/unbatch.py
@@ -1,4 +1,4 @@
-from typing import List
+from typing import List, Optional
 
 import torch
 from torch import Tensor
@@ -6,7 +6,12 @@
 from torch_geometric.utils import degree
 
 
-def unbatch(src: Tensor, batch: Tensor, dim: int = 0) -> List[Tensor]:
+def unbatch(
+    src: Tensor,
+    batch: Tensor,
+    dim: int = 0,
+    batch_size: Optional[int] = None,
+) -> List[Tensor]:
     r"""Splits :obj:`src` according to a :obj:`batch` vector along dimension
     :obj:`dim`.
 
@@ -17,6 +22,7 @@ def unbatch(src: Tensor, batch: Tensor, dim: int = 0) -> List[Tensor]:
             entry in :obj:`src` to a specific example. Must be ordered.
         dim (int, optional): The dimension along which to split the :obj:`src`
             tensor. (default: :obj:`0`)
+        batch_size (int, optional) The batch size. (default: :obj:`None`)
 
     :rtype: :class:`List[Tensor]`
 
@@ -27,11 +33,15 @@ def unbatch(src: Tensor, batch: Tensor, dim: int = 0) -> List[Tensor]:
         >>> unbatch(src, batch)
         (tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6]))
     """
-    sizes = degree(batch, dtype=torch.long).tolist()
+    sizes = degree(batch, batch_size, dtype=torch.long).tolist()
     return src.split(sizes, dim)
 
 
-def unbatch_edge_index(edge_index: Tensor, batch: Tensor) -> List[Tensor]:
+def unbatch_edge_index(
+    edge_index: Tensor,
+    batch: Tensor,
+    batch_size: Optional[int] = None,
+) -> List[Tensor]:
     r"""Splits the :obj:`edge_index` according to a :obj:`batch` vector.
 
     Args:
@@ -39,6 +49,7 @@ def unbatch_edge_index(edge_index: Tensor, batch: Tensor) -> List[Tensor]:
         batch (LongTensor): The batch vector
             :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each
             node to a specific example. Must be ordered.
+        batch_size (int, optional) The batch size. (default: :obj:`None`)
 
     :rtype: :class:`List[Tensor]`
 
@@ -53,10 +64,10 @@ def unbatch_edge_index(edge_index: Tensor, batch: Tensor) -> List[Tensor]:
         tensor([[0, 1, 1, 2],
                 [1, 0, 2, 1]]))
     """
-    deg = degree(batch, dtype=torch.int64)
+    deg = degree(batch, batch_size, dtype=torch.long)
     ptr = torch.cat([deg.new_zeros(1), deg.cumsum(dim=0)[:-1]], dim=0)
 
     edge_batch = batch[edge_index[0]]
     edge_index = edge_index - ptr[edge_batch]
-    sizes = degree(edge_batch, dtype=torch.int64).cpu().tolist()
+    sizes = degree(edge_batch, batch_size, dtype=torch.long).cpu().tolist()
     return edge_index.split(sizes, dim=1)