diff --git a/examples/graphbolt/pyg/hetero/node_classification.py b/examples/graphbolt/pyg/hetero/node_classification.py
index 032b84d82c..4166805c3f 100644
--- a/examples/graphbolt/pyg/hetero/node_classification.py
+++ b/examples/graphbolt/pyg/hetero/node_classification.py
@@ -58,12 +58,12 @@ def create_dataloader(
         datapipe = datapipe.copy_to(device=device)
         need_copy = False
 
+    node_feature_keys = {"paper": ["feat"], "author": ["feat"]}
     if args.dataset == "ogb-lsc-mag240m":
-        node_feature_keys = {
-            "paper": ["feat"],
-            "author": ["feat"],
-            "institution": ["feat"],
-        }
+        node_feature_keys["institution"] = ["feat"]
+    if "igb-het" in args.dataset:
+        node_feature_keys["institute"] = ["feat"]
+        node_feature_keys["fos"] = ["feat"]
     # Fetch node features for the sampled subgraph.
     datapipe = datapipe.fetch_feature(features, node_feature_keys)
 
@@ -335,8 +335,13 @@ def parse_args():
         "--dataset",
         type=str,
         default="ogb-lsc-mag240m",
-        choices=["ogb-lsc-mag240m"],
-        help="Dataset name. Possible values: ogb-lsc-mag240m",
+        choices=[
+            "ogb-lsc-mag240m",
+            "igb-het-tiny",
+            "igb-het-small",
+            "igb-het-medium",
+        ],
+        help="Dataset name. Possible values: ogb-lsc-mag240m, igb-het-[tiny|small|medium].",
     )
     parser.add_argument(
         "--fanout",
diff --git a/python/dgl/graphbolt/impl/ondisk_dataset.py b/python/dgl/graphbolt/impl/ondisk_dataset.py
index 6783bb296a..303a423a85 100644
--- a/python/dgl/graphbolt/impl/ondisk_dataset.py
+++ b/python/dgl/graphbolt/impl/ondisk_dataset.py
@@ -990,6 +990,16 @@ class BuiltinDataset(OnDiskDataset):
             Self edges are added to the original graph.
             Node features are stored as float32.
 
+    **igb-het-[tiny|small|medium]**
+        The igb-hom-[tiny|small|medium] dataset is a heterogeneous citation network,
+        which is designed for developers to train and evaluate GNN models with
+        high fidelity. See more details in `igb-het-[tiny|small|medium]
+        <https://github.com/IllinoisGraphBenchmark/IGB-Datasets>`_.
+
+        .. note::
+            Four Reverse edge types are added to the original graph.
+            Node features are stored as float32.
+
     Parameters
     ----------
     name : str
@@ -1019,6 +1029,10 @@ class BuiltinDataset(OnDiskDataset):
         "igb-hom-tiny-seeds",
         "igb-hom-small",
         "igb-hom-small-seeds",
+        "igb-het-tiny",
+        "igb-het-tiny-seeds",
+        "igb-het-small",
+        "igb-het-small-seeds",
     ]
     _large_datasets = [
         "ogb-lsc-mag240m",
@@ -1031,6 +1045,8 @@ class BuiltinDataset(OnDiskDataset):
         "igb-hom-large-seeds",
         "igb-hom",
         "igb-hom-seeds",
+        "igb-het-medium",
+        "igb-het-medium-seeds",
     ]
     _all_datasets = _datasets + _large_datasets