diff --git a/dgf/src/learning/ten_lines/node_prediction_dataset.py b/dgf/src/learning/ten_lines/node_prediction_dataset.py
index 4531e84..33dcf03 100644
--- a/dgf/src/learning/ten_lines/node_prediction_dataset.py
+++ b/dgf/src/learning/ten_lines/node_prediction_dataset.py
@@ -625,6 +625,8 @@ def compute_train_and_valid_node_idxs(
     train_seed_nodes: Optional[common.SeedNodeIdxs],
     valid_seed_nodes: Optional[common.SeedNodeIdxs],
     max_num_valid_examples: Optional[int],
+    temporal_split: bool = False,
+    ts_feature: Optional[str] = None,
 ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
   """Computes the training and validation seed node indices."""
   if not isinstance(graph, in_memory_graph_lib.InMemoryGraph) or (
@@ -670,12 +672,39 @@ def compute_train_and_valid_node_idxs(
     )
     return None, None
 
-  train_seed_node_idxs, valid_seed_node_idxs = util.split_train_valid(
-      num_graph_seed_nodes,
-      validation_ratio,
-      random_seed,
-      max_num_valid_examples=max_num_valid_examples,
-  )
+  if temporal_split:
+    if ts_feature is None:
+      raise ValueError(
+          "`ts_feature` must be specified when `temporal_split` is True."
+      )
+    if ts_feature not in graph.node_sets[target_nodeset].features:
+      raise ValueError(
+          f"Timestamp feature '{ts_feature}' not found in"
+          f" target nodeset '{target_nodeset}'."
+      )
+    log.info(
+        "Splitting the train and validation sets temporally on the creation"
+        " timestamp feature %r of nodeset %r: the oldest nodes are used for"
+        " training and the most recent ones for validation (instead of a"
+        " random split).",
+        ts_feature,
+        target_nodeset,
+    )
+    timestamps = graph.node_sets[target_nodeset].features[ts_feature]
+    train_seed_node_idxs, valid_seed_node_idxs = (
+        util.split_train_valid_temporal(
+            creation_times=timestamps,
+            validation_ratio=validation_ratio,
+            max_num_valid_examples=max_num_valid_examples,
+        )
+    )
+  else:
+    train_seed_node_idxs, valid_seed_node_idxs = util.split_train_valid(
+        num_graph_seed_nodes,
+        validation_ratio,
+        random_seed,
+        max_num_valid_examples=max_num_valid_examples,
+    )
   log.info(
       "Num. training seed nodes: %d, Num. validation seed nodes: %d",
       len(train_seed_node_idxs),
@@ -715,6 +744,11 @@ def prepare_datasets(
   else:
     max_num_valid_examples = num_valid_steps * batch_size
 
+  target_ts_feature = temporal_util.creation_time_feature_name(
+      schema.node_sets[target_nodeset].features
+  )
+  target_has_creation_time = target_ts_feature is not None
+
   train_seed_node_idxs, valid_seed_node_idxs = (
       compute_train_and_valid_node_idxs(
           graph,
@@ -726,6 +760,8 @@ def prepare_datasets(
           train_seed_nodes=train_seed_nodes,
           valid_seed_nodes=valid_seed_nodes,
           max_num_valid_examples=max_num_valid_examples,
+          temporal_split=temporal_sampling and target_has_creation_time,
+          ts_feature=target_ts_feature,
       )
   )
 
@@ -740,13 +776,6 @@ def prepare_datasets(
     sampling_plan = sampling_config_lib.simple_sampling_config_to_sampling_plan(
         sampling_config, schema
     )
-
-  target_has_creation_time = (
-      temporal_util.creation_time_feature_name(
-          schema.node_sets[target_nodeset].features
-      )
-      is not None
-  )
   if auto_normalize_config is None:
     auto_normalize_config = normalize_lib.AutoNormalizeConfig(
         keep_raw_features=keep_raw_features or set(),
diff --git a/dgf/src/learning/ten_lines/node_prediction_dataset_test.py b/dgf/src/learning/ten_lines/node_prediction_dataset_test.py
index 614efde..cbd0ede 100644
--- a/dgf/src/learning/ten_lines/node_prediction_dataset_test.py
+++ b/dgf/src/learning/ten_lines/node_prediction_dataset_test.py
@@ -429,6 +429,117 @@ class root:
           schema=schema_without_ts,
       )
 
+  def test_compute_train_and_valid_node_idxs(self):
+    graph, _ = gen_test_graph.generate_temporal_in_memory_graph(False)
+    # 1. Temporal split
+    train_idx, valid_idx = (
+        node_prediction_dataset.compute_train_and_valid_node_idxs(
+            graph=graph,
+            valid_graph=None,
+            graph_format="IN_MEMORY_GRAPH",
+            target_nodeset="n1",
+            random_seed=42,
+            validation_ratio=0.5,
+            train_seed_nodes=None,
+            valid_seed_nodes=None,
+            max_num_valid_examples=None,
+            temporal_split=True,
+            ts_feature="timestamp",
+        )
+    )
+    self.assertIsNotNone(train_idx)
+    self.assertIsNotNone(valid_idx)
+    np.testing.assert_array_equal(train_idx, np.array([0, 1]))
+    np.testing.assert_array_equal(valid_idx, np.array([2, 3]))
+
+    # 2. Random split (temporal_split=False)
+    train_idx, valid_idx = (
+        node_prediction_dataset.compute_train_and_valid_node_idxs(
+            graph=graph,
+            valid_graph=None,
+            graph_format="IN_MEMORY_GRAPH",
+            target_nodeset="n1",
+            random_seed=42,
+            validation_ratio=0.5,
+            train_seed_nodes=None,
+            valid_seed_nodes=None,
+            max_num_valid_examples=None,
+            temporal_split=False,
+        )
+    )
+    self.assertIsNotNone(train_idx)
+    self.assertIsNotNone(valid_idx)
+    self.assertLen(train_idx, 2)
+    self.assertLen(valid_idx, 2)
+
+    # 3. Error when temporal_split=True but ts_feature is None
+    with self.assertRaises(ValueError):
+      node_prediction_dataset.compute_train_and_valid_node_idxs(
+          graph=graph,
+          valid_graph=None,
+          graph_format="IN_MEMORY_GRAPH",
+          target_nodeset="n1",
+          random_seed=42,
+          validation_ratio=0.5,
+          train_seed_nodes=None,
+          valid_seed_nodes=None,
+          max_num_valid_examples=None,
+          temporal_split=True,
+          ts_feature=None,
+      )
+
+    # 4. Error when temporal_split=True but ts_feature is not found in graph
+    with self.assertRaises(ValueError):
+      node_prediction_dataset.compute_train_and_valid_node_idxs(
+          graph=graph,
+          valid_graph=None,
+          graph_format="IN_MEMORY_GRAPH",
+          target_nodeset="n1",
+          random_seed=42,
+          validation_ratio=0.5,
+          train_seed_nodes=None,
+          valid_seed_nodes=None,
+          max_num_valid_examples=None,
+          temporal_split=True,
+          ts_feature="non_existent_feature",
+      )
+
+  def test_prepare_datasets_automatic_temporal_split(self):
+    graph, schema = gen_test_graph.generate_temporal_in_memory_graph(False)
+    train_dataset, valid_dataset = node_prediction_dataset.prepare_datasets(
+        graph=graph,
+        valid_graph=None,  # pyrefly: ignore[bad-argument-type]
+        schema=schema,
+        target_nodeset="n1",
+        random_seed=42,
+        batch_size=2,
+        num_sampling_hops=1,
+        sampling_width=3,
+        verbose=0,
+        graph_format="IN_MEMORY_GRAPH",
+        validation_ratio=0.5,
+        train_seed_nodes=None,
+        valid_seed_nodes=None,
+        temporal_sampling=True,
+        nodeset_timestamp_features={"n1": "timestamp"},
+        edgeset_timestamp_features={"e1": "timestamp"},
+        num_valid_steps=None,
+        cache_valid_dataset=False,
+        cache_normalized_features=False,
+        cache_normalized_features_device="host",
+        sampling_plan=None,
+    )
+    self.assertIsNotNone(train_dataset.seed_node_idxs)
+    self.assertIsNotNone(valid_dataset)
+    assert valid_dataset is not None
+    self.assertIsNotNone(valid_dataset.seed_node_idxs)
+    np.testing.assert_array_equal(
+        np.sort(train_dataset.seed_node_idxs), np.array([0, 1])
+    )
+    np.testing.assert_array_equal(
+        np.sort(valid_dataset.seed_node_idxs), np.array([2, 3])
+    )
+
 
 if __name__ == "__main__":
   absltest.main()
diff --git a/dgf/src/learning/ten_lines/node_prediction_train.py b/dgf/src/learning/ten_lines/node_prediction_train.py
index 477204e..9ae0acf 100644
--- a/dgf/src/learning/ten_lines/node_prediction_train.py
+++ b/dgf/src/learning/ten_lines/node_prediction_train.py
@@ -195,8 +195,10 @@ def train_node_model(
     verbose: The verbosity level. Higher values provide more output.
     validation_ratio: Ratio of the training dataset used to create the
       validation dataset in case no validation dataset is manually provided
-      e.g., train_seed_nodes and valid_seed_nodes are provided. If set to 0, the
-      entire dataset is used for training, and the tree is not pruned.
+      e.g., train_seed_nodes and valid_seed_nodes are provided. If `time_aware`
+      is True, the split is temporal (past nodes for training, future nodes for
+      validation) instead of random. If set to 0, the entire dataset is used
+      for training, and the tree is not pruned.
     train_seed_nodes: Optional. A np.ndarray or list of integer indices
       specifying the subset of nodes within the `target_nodeset` to be used for
       training. If None, the training nodes are determined based on
@@ -227,7 +229,9 @@ def train_node_model(
       time-consuming, but it will increase memory usage.
     time_aware: Enables temporal-aware training. If `False` (default), no
       temporal masking is applied. If `True`, timestamp features are inferred
-      from the schema (via features marked as creation timestamps).
+      from the schema (via features marked as creation timestamps), the target
+      nodeset is required to have a creation timestamp, and the train and
+      validation sets are split temporally instead of randomly.
     message_pooling: The pooling method to use for aggregating messages.
     experimental_preprocess_core_model_config: Advanced option. An optional
       callable to modify the `CoreModelConfig` before it is used to build the