diff --git a/examples/temporal_link_pred_graph_mixer.py b/examples/temporal_link_pred_graph_mixer.py
new file mode 100644
index 000000000000..78d9a6076c83
--- /dev/null
+++ b/examples/temporal_link_pred_graph_mixer.py
@@ -0,0 +1,131 @@
+# TODO: move model to torch_geometric.nn.models.graph_mixer
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+
+import torch_geometric
+import torch_geometric.transforms as T
+from torch_geometric.datasets import GDELTLite, Planetoid
+from torch_geometric.loader import LinkNeighborLoader, NeighborLoader
+from torch_geometric.nn.models.graph_mixer import LinkEncoder, NodeEncoder
+from torch_geometric.utils import to_undirected
+
+
+class GraphMixer(torch.nn.Module):
+    def __init__(
+        self,
+        num_node_feats: int,
+        num_edge_feats: int,
+        link_encoder_k: int = 30,
+        link_encoder_hidden_channels: int = 12,
+        link_encoder_out_channels: int = 34,
+        link_encoder_time_channels=56,
+        node_encoder_time_window: int = 78,
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.link_encoder = LinkEncoder(
+            k=link_encoder_k,
+            in_channels=num_edge_feats,
+            hidden_channels=link_encoder_hidden_channels,
+            out_channels=link_encoder_out_channels,
+            time_channels=link_encoder_time_channels,
+            is_sorted=False,
+            dropout=dropout,
+        )
+        self.node_encoder = NodeEncoder(time_window=node_encoder_time_window)
+        self.link_classifier = torch.nn.Linear(
+            (link_encoder_out_channels + num_node_feats) * 2, 1)
+
+    def forward(
+        self,
+        x,
+        edge_index,
+        edge_attr,
+        edge_time,
+        seed_time,
+        edge_label_index,
+    ):
+        # [num_nodes, link_encoder_out_channels]
+        link_feat = self.link_encoder(
+            edge_index,
+            edge_attr,
+            edge_time,
+            seed_time,
+        )
+
+        # [num_nodes, num_node_feats]
+        node_feat = self.node_encoder(
+            x,
+            edge_index,
+            edge_time,
+            seed_time,
+        )
+
+        # [num_nodes, link_encoder_out_channels + num_node_feats]
+        feats = torch.cat([link_feat, node_feat], dim=-1)
+
+        # TODO: Filter out non-root nodes earlier than here if possible
+        # [batch_size, dim]
+        feats_src = feats[edge_label_index[0]]
+        # [batch_size, dim]
+        feats_dst = feats[edge_label_index[1]]
+        feat_pairs = torch.cat([feats_src, feats_dst], dim=-1)
+
+        # [batch_size, 1]
+        out = self.link_classifier(feat_pairs).squeeze(-1)
+        return out
+
+
+def main():
+    # TODO: Split train/val/test
+    data = GDELTLite("data")[0]
+    # describe_data(data)
+
+    # TODO: Enable negative sampling
+    K = 2
+    loader = LinkNeighborLoader(
+        data,
+        num_neighbors=[7],
+        # num_neighbors=[-1]  # to only use K most recent ones in the model
+        # neg_sampling_ratio=0.0,
+        edge_label=torch.ones(data.num_edges),
+        time_attr="edge_time",
+        edge_label_time=data.edge_time,
+        batch_size=13,
+        shuffle=True,
+    )
+    model = GraphMixer(
+        num_node_feats=data.x.size(1),
+        num_edge_feats=data.edge_attr.size(1),
+    )
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+
+    for _ in range(100):
+        train_loss = 0.0
+        model.train()
+        for sampled_data in tqdm(loader):
+            # sampled_data: num_edges == batch_size * K
+            optimizer.zero_grad()
+            pred = model(
+                sampled_data.x,
+                sampled_data.edge_index,
+                sampled_data.edge_attr.to(torch.float),
+                sampled_data.edge_time.to(torch.float),
+                sampled_data.edge_label_time,
+                sampled_data.edge_label_index,
+            )
+            loss = F.binary_cross_entropy_with_logits(
+                pred,
+                sampled_data.edge_label,
+            )
+            loss.backward()
+            optimizer.step()
+            train_loss += loss.item()
+            print(loss.item())
+            break
+        break
+
+
+if __name__ == "__main__":
+    main()
diff --git a/torch_geometric/nn/models/graph_mixer.py b/torch_geometric/nn/models/graph_mixer.py
index 75680efd7267..fd53db66a4a1 100644
--- a/torch_geometric/nn/models/graph_mixer.py
+++ b/torch_geometric/nn/models/graph_mixer.py
@@ -6,6 +6,7 @@
 
 from torch_geometric.nn import TemporalEncoding
 from torch_geometric.utils import scatter, to_dense_batch
+from torch_geometric.utils.num_nodes import maybe_num_nodes
 
 
 class NodeEncoder(torch.nn.Module):
@@ -261,7 +262,7 @@ def forward(
             edge_index=edge_index,
             edge_attr=edge_attr,
             edge_time=edge_time,
-            num_nodes=seed_time.size(0),
+            num_nodes=maybe_num_nodes(edge_index),
             is_sorted=self.is_sorted,
         )