diff --git a/python/graphstorm/config/argument.py b/python/graphstorm/config/argument.py
index 494ddf8fbf..61fee036c1 100644
--- a/python/graphstorm/config/argument.py
+++ b/python/graphstorm/config/argument.py
@@ -1577,6 +1577,8 @@ def target_ntype(self):
         if hasattr(self, "_target_ntype"):
             return self._target_ntype
         else:
+            logging.warning("There is not target ntype provided, "
+                            "will treat as homogeneous graph")
             return DEFAULT_NTYPE
 
     @property
@@ -1650,8 +1652,10 @@ def target_etype(self):
             classification/regression. Support multiple tasks when needed.
         """
         # pylint: disable=no-member
-        assert hasattr(self, "_target_etype"), \
-            "Edge classification task needs a target etype"
+        if not hasattr(self, "_target_etype"):
+            logging.warning("There is not target etype provided, "
+                            "will treat as homogeneous graph")
+            return DEFAULT_ETYPE
         assert isinstance(self._target_etype, list), \
             "target_etype must be a list in format: " \
             "[\"query,clicks,asin\", \"query,search,asin\"]."
diff --git a/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py b/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py
index 661c9e14d3..cc82cf3ce2 100644
--- a/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py
+++ b/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py
@@ -70,6 +70,13 @@ def main(config_args):
                                     label_field=config.label_field,
                                     decoder_edge_feat=config.decoder_edge_feat,
                                     lm_feat_ntypes=get_lm_ntypes(config.node_lm_configs))
+    if config.target_etype == DEFAULT_ETYPE:
+        assert train_data.g.ntypes == [DEFAULT_NTYPE] and \
+               train_data.g.etypes == [DEFAULT_ETYPE[1]], \
+            f"It is required to be a homogeneous graph when not providing " \
+            f"target_ntype on node task, expect node type {[DEFAULT_NTYPE]} and " \
+            f"edge type {[DEFAULT_ETYPE[1]]}, but get {train_data.g.ntypes} " \
+            f"and {train_data.g.etypes}"
     model = gs.create_builtin_edge_gnn_model(train_data.g, config, train_task=True)
     trainer = GSgnnEdgePredictionTrainer(model, topk_model_to_save=config.topk_model_to_save)
     if config.restore_model_path is not None: