diff --git a/test/data/test_edge_index.py b/test/data/test_edge_index.py
index 6e122e50c800..27ff274e7cea 100644
--- a/test/data/test_edge_index.py
+++ b/test/data/test_edge_index.py
@@ -82,6 +82,17 @@ def test_share_memory():
     assert adj._rowptr.is_shared()
 
 
+def test_contiguous():
+    data = torch.tensor([[0, 1], [1, 0], [1, 2], [2, 1]]).t()
+
+    with pytest.raises(ValueError, match="needs to be contiguous"):
+        EdgeIndex(data)
+
+    adj = EdgeIndex(data.contiguous()).contiguous()
+    assert isinstance(adj, EdgeIndex)
+    assert adj.is_contiguous()
+
+
 def test_sort_by():
     adj = EdgeIndex([[0, 1, 1, 2], [1, 0, 2, 1]], sort_order='row')
     out = adj.sort_by('row')
diff --git a/torch_geometric/data/edge_index.py b/torch_geometric/data/edge_index.py
index 4011a6d99e3c..2a2bf38c70e8 100644
--- a/torch_geometric/data/edge_index.py
+++ b/torch_geometric/data/edge_index.py
@@ -49,6 +49,12 @@ def assert_two_dimensional(tensor: Tensor):
                          f"[2, *] (got {list(tensor.size())})")
 
 
+def assert_contiguous(tensor: Tensor):
+    if not tensor.is_contiguous():
+        raise ValueError("'EdgeIndex' needs to be contiguous. Please call "
+                         "`edge_index.contiguous()` before proceeding.")
+
+
 class EdgeIndex(Tensor):
     r"""An advanced :obj:`edge_index` representation with additional (meta)data
     attached.
@@ -114,6 +120,7 @@ def __new__(
         assert isinstance(data, Tensor)
         assert_valid_dtype(data)
         assert_two_dimensional(data)
+        assert_contiguous(data)
 
         out = super().__new__(cls, data)
 
@@ -130,6 +137,7 @@ def validate(self) -> 'EdgeIndex':
         """
         assert_valid_dtype(self)
         assert_two_dimensional(self)
+        assert_contiguous(self)
 
         if self.numel() > 0 and self.min() < 0:
             raise ValueError(f"'{self.__class__.__name__}' contains negative "
@@ -346,6 +354,11 @@ def share_memory_(tensor: EdgeIndex) -> EdgeIndex:
     return apply_(tensor, Tensor.share_memory_)
 
 
+@implements(Tensor.contiguous)
+def contiguous(tensor: EdgeIndex) -> EdgeIndex:
+    return apply_(tensor, Tensor.contiguous)
+
+
 @implements(torch.cat)
 def cat(
     tensors: List[Union[EdgeIndex, Tensor]],