diff --git a/CHANGELOG.md b/CHANGELOG.md
index 65ee963b8a62..1316c353fcfe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Fixed `subgraph` on unordered inputs ([#7187](https://github.com/pyg-team/pytorch_geometric/pull/7187))
 - Allow missing node types in `HeteroDictLinear` ([#7185](https://github.com/pyg-team/pytorch_geometric/pull/7185))
 - Optimized `from_networkx` memory footprint by reducing unnecessary copies ([#7119](https://github.com/pyg-team/pytorch_geometric/pull/7119))
 - Added an optional `batch_size` argument to `LayerNorm`, `GraphNorm`, `InstanceNorm`, `GraphSizeNorm` and `PairNorm` ([#7135](https://github.com/pyg-team/pytorch_geometric/pull/7135))
diff --git a/torch_geometric/utils/subgraph.py b/torch_geometric/utils/subgraph.py
index b20a645b6163..490e53a6b78f 100644
--- a/torch_geometric/utils/subgraph.py
+++ b/torch_geometric/utils/subgraph.py
@@ -87,13 +87,13 @@ def subgraph(
     if isinstance(subset, (list, tuple)):
         subset = torch.tensor(subset, dtype=torch.long, device=device)
 
-    if subset.dtype == torch.bool or subset.dtype == torch.uint8:
-        num_nodes = subset.size(0)
-    else:
+    if subset.dtype != torch.bool:
         num_nodes = maybe_num_nodes(edge_index, num_nodes)
-        subset = index_to_mask(subset, size=num_nodes)
+        node_mask = index_to_mask(subset, size=num_nodes)
+    else:
+        num_nodes = subset.size(0)
+        node_mask = subset
 
-    node_mask = subset
     edge_mask = node_mask[edge_index[0]] & node_mask[edge_index[1]]
     edge_index = edge_index[:, edge_mask]
     edge_attr = edge_attr[edge_mask] if edge_attr is not None else None
@@ -101,7 +101,7 @@ def subgraph(
     if relabel_nodes:
         node_idx = torch.zeros(node_mask.size(0), dtype=torch.long,
                                device=device)
-        node_idx[subset] = torch.arange(subset.sum().item(), device=device)
+        node_idx[subset] = torch.arange(node_mask.sum().item(), device=device)
         edge_index = node_idx[edge_index]
 
     if return_edge_mask:
@@ -168,22 +168,28 @@ def bipartite_subgraph(
 
     if src_subset.dtype != torch.bool:
         src_size = int(edge_index[0].max()) + 1 if size is None else size[0]
-        src_subset = index_to_mask(src_subset, size=src_size)
+        src_node_mask = index_to_mask(src_subset, size=src_size)
+    else:
+        src_size = src_subset.size(0)
+        src_node_mask = src_subset
+
     if dst_subset.dtype != torch.bool:
         dst_size = int(edge_index[1].max()) + 1 if size is None else size[1]
-        dst_subset = index_to_mask(dst_subset, size=dst_size)
+        dst_node_mask = index_to_mask(dst_subset, size=dst_size)
+    else:
+        dst_size = dst_subset.size(0)
+        dst_node_mask = dst_subset
 
-    # node_mask = subset
-    edge_mask = src_subset[edge_index[0]] & dst_subset[edge_index[1]]
+    edge_mask = src_node_mask[edge_index[0]] & dst_node_mask[edge_index[1]]
     edge_index = edge_index[:, edge_mask]
     edge_attr = edge_attr[edge_mask] if edge_attr is not None else None
 
     if relabel_nodes:
-        node_idx_i = edge_index.new_zeros(src_subset.size(0))
-        node_idx_j = edge_index.new_zeros(dst_subset.size(0))
-        node_idx_i[src_subset] = torch.arange(int(src_subset.sum()),
+        node_idx_i = edge_index.new_zeros(src_node_mask.size(0))
+        node_idx_j = edge_index.new_zeros(dst_node_mask.size(0))
+        node_idx_i[src_subset] = torch.arange(int(src_node_mask.sum()),
                                               device=node_idx_i.device)
-        node_idx_j[dst_subset] = torch.arange(int(dst_subset.sum()),
+        node_idx_j[dst_subset] = torch.arange(int(dst_node_mask.sum()),
                                               device=node_idx_j.device)
         edge_index = torch.stack([
             node_idx_i[edge_index[0]],