diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5d782d9ec530..1f44c14dea2a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [2.4.0] - 2023-MM-DD
 
 ### Added
+
+- Added unbatching logic for `torch.sparse` tensors ([#7037](https://github.com/pyg-team/pytorch_geometric/pull/7037))
 - Added the `RotatE` KGE model ([#7026](https://github.com/pyg-team/pytorch_geometric/pull/7026))
 
 ### Changed
diff --git a/test/data/test_batch.py b/test/data/test_batch.py
index 5a521c03e00b..fc63c16c70fc 100644
--- a/test/data/test_batch.py
+++ b/test/data/test_batch.py
@@ -166,6 +166,48 @@ def test_batch_with_sparse_tensor():
     assert data_list[2].adj.coo()[1].tolist() == [1, 0, 2, 1, 3, 2]
 
 
+def test_batch_with_torch_coo_tensor():
+    x = torch.tensor([[1.0], [2.0], [3.0]]).to_sparse_coo()
+    data1 = Data(x=x)
+
+    x = torch.tensor([[1.0], [2.0]]).to_sparse_coo()
+    data2 = Data(x=x)
+
+    x = torch.tensor([[1.0], [2.0], [3.0], [4.0]]).to_sparse_coo()
+    data3 = Data(x=x)
+
+    batch = Batch.from_data_list([data1])
+    assert str(batch) == ('DataBatch(x=[3, 1], batch=[3], ptr=[2])')
+    assert batch.num_graphs == len(batch) == 1
+    assert batch.x.to_dense().tolist() == [[1], [2], [3]]
+    assert batch.batch.tolist() == [0, 0, 0]
+    assert batch.ptr.tolist() == [0, 3]
+
+    batch = Batch.from_data_list([data1, data2, data3])
+
+    assert str(batch) == ('DataBatch(x=[9, 1], batch=[9], ptr=[4])')
+    assert batch.num_graphs == len(batch) == 3
+    assert batch.x.to_dense().view(-1).tolist() == [1, 2, 3, 1, 2, 1, 2, 3, 4]
+    assert batch.batch.tolist() == [0, 0, 0, 1, 1, 2, 2, 2, 2]
+    assert batch.ptr.tolist() == [0, 3, 5, 9]
+
+    assert str(batch[0]) == ("Data(x=[3, 1])")
+    assert str(batch[1]) == ("Data(x=[2, 1])")
+    assert str(batch[2]) == ("Data(x=[4, 1])")
+
+    data_list = batch.to_data_list()
+    assert len(data_list) == 3
+
+    assert len(data_list[0]) == 1
+    assert data_list[0].x.to_dense().tolist() == [[1], [2], [3]]
+
+    assert len(data_list[1]) == 1
+    assert data_list[1].x.to_dense().tolist() == [[1], [2]]
+
+    assert len(data_list[2]) == 1
+    assert data_list[2].x.to_dense().tolist() == [[1], [2], [3], [4]]
+
+
 def test_batching_with_new_dimension():
     torch_geometric.set_debug(True)
 
diff --git a/torch_geometric/data/separate.py b/torch_geometric/data/separate.py
index a3850a251198..5fcb98b9203c 100644
--- a/torch_geometric/data/separate.py
+++ b/torch_geometric/data/separate.py
@@ -6,6 +6,7 @@
 from torch_geometric.data.data import BaseData
 from torch_geometric.data.storage import BaseStorage
 from torch_geometric.typing import SparseTensor
+from torch_geometric.utils import narrow
 
 
 def separate(cls, batch: BaseData, idx: int, slice_dict: Any,
@@ -62,7 +63,7 @@ def _separate(
         key = str(key)
         cat_dim = batch.__cat_dim__(key, value, store)
         start, end = int(slices[idx]), int(slices[idx + 1])
-        value = value.narrow(cat_dim or 0, start, end - start)
+        value = narrow(value, cat_dim or 0, start, end - start)
         value = value.squeeze(0) if cat_dim is None else value
         if decrement and (incs.dim() > 1 or int(incs[idx]) != 0):
             value = value - incs[idx].to(value.device)
diff --git a/torch_geometric/utils/select.py b/torch_geometric/utils/select.py
index 210b127df2cc..54d0a3961f5d 100644
--- a/torch_geometric/utils/select.py
+++ b/torch_geometric/utils/select.py
@@ -4,6 +4,7 @@
 from torch import Tensor
 
 from torch_geometric.utils.mask import mask_select
+from torch_geometric.utils.sparse import is_torch_sparse_tensor
 
 
 def select(src: Union[Tensor, List[Any]], index_or_mask: Tensor,
@@ -41,6 +42,11 @@ def narrow(src: Union[Tensor, List[Any]], dim: int, start: int,
         start (int): The starting dimension.
         length (int): The distance to the ending dimension.
     """
+    if is_torch_sparse_tensor(src):
+        # TODO Sparse tensors in `torch.sparse` do not yet support `narrow`.
+        index = torch.arange(start, start + length, device=src.device)
+        return src.index_select(dim, index)
+
     if isinstance(src, Tensor):
         return src.narrow(dim, start, length)