diff --git a/examples/explain/gnn_explainer.py b/examples/explain/gnn_explainer.py
index 22ffa2462778..3e28bef2fafc 100644
--- a/examples/explain/gnn_explainer.py
+++ b/examples/explain/gnn_explainer.py
@@ -60,5 +60,6 @@ def forward(self, x, edge_index):
 print(f"Feature importance plot has been saved to '{path}'")
 
 path = 'subgraph.pdf'
-explanation.visualize_graph(path)
+explanation.visualize_graph(path, node_label=None, color_dict=None,
+                            target_node=node_index, draw_node_idx=True)
 print(f"Subgraph visualization plot has been saved to '{path}'")
diff --git a/examples/explain/gnn_explainer_ba_shapes.py b/examples/explain/gnn_explainer_ba_shapes.py
index d9c82a3cfae5..6913493223b7 100644
--- a/examples/explain/gnn_explainer_ba_shapes.py
+++ b/examples/explain/gnn_explainer_ba_shapes.py
@@ -80,6 +80,9 @@ def test():
 
     # Explanation ROC AUC over all test nodes:
     targets, preds = [], []
+    # The color for each node class label. For BAshape dataset, according to GNNExplainer Fig.3
+    color_dict = {0: 'orange', 1: 'green', 2: 'green', 3: 'green'}
+
     node_indices = range(400, data.num_nodes, 5)
     for node_index in tqdm(node_indices, leave=False, desc='Train Explainer'):
         target = data.y if explanation_type == 'phenomenon' else None
@@ -94,3 +97,14 @@ def test():
 
     auc = roc_auc_score(torch.cat(targets), torch.cat(preds))
     print(f'Mean ROC AUC (explanation type {explanation_type:10}): {auc:.4f}')
+
+node_index = 500
+explanation = explainer(data.x, data.edge_index, index=node_index,
+                        target=data.y)
+explanation.visualize_graph(
+    f"GNNExplainer_BAshapes_{node_index}.png",
+    node_label=data.y,
+    color_dict=color_dict,
+    target_node=node_index,
+    draw_node_idx=False,
+)
diff --git a/torch_geometric/explain/explanation.py b/torch_geometric/explain/explanation.py
index 8897a32d166a..1aaac9281fa5 100644
--- a/torch_geometric/explain/explanation.py
+++ b/torch_geometric/explain/explanation.py
@@ -233,7 +233,7 @@ def visualize_feature_importance(
         return _visualize_score(score, feat_labels, path, top_k)
 
     def visualize_graph(self, path: Optional[str] = None,
-                        backend: Optional[str] = None):
+                        backend: Optional[str] = None, **kwargs):
         r"""Visualizes the explanation graph with edge opacity corresponding to
         edge importance.
 
@@ -246,13 +246,14 @@ def visualize_graph(self, path: Optional[str] = None,
                 If set to :obj:`None`, will use the most appropriate
                 visualization backend based on available system packages.
                 (default: :obj:`None`)
+            **kwargs: include
         """
         edge_mask = self.get('edge_mask')
         if edge_mask is None:
             raise ValueError(f"The attribute 'edge_mask' is not available "
                              f"in '{self.__class__.__name__}' "
                              f"(got {self.available_explanations})")
-        visualize_graph(self.edge_index, edge_mask, path, backend)
+        visualize_graph(self.edge_index, edge_mask, path, backend, **kwargs)
 
 
 class HeteroExplanation(HeteroData, ExplanationMixin):
diff --git a/torch_geometric/visualization/graph.py b/torch_geometric/visualization/graph.py
index 9225184df3ff..85063bb375e9 100644
--- a/torch_geometric/visualization/graph.py
+++ b/torch_geometric/visualization/graph.py
@@ -26,6 +26,7 @@ def visualize_graph(
     edge_weight: Optional[Tensor] = None,
     path: Optional[str] = None,
     backend: Optional[str] = None,
+    **kwargs,
 ) -> Any:
     r"""Visualizes the graph given via :obj:`edge_index` and (optional)
     :obj:`edge_weight`.
@@ -58,7 +59,8 @@ def visualize_graph(
         backend = 'graphviz' if has_graphviz() else 'networkx'
 
     if backend.lower() == 'networkx':
-        return _visualize_graph_via_networkx(edge_index, edge_weight, path)
+        return _visualize_graph_via_networkx(edge_index, edge_weight, path,
+                                             **kwargs)
     elif backend.lower() == 'graphviz':
         return _visualize_graph_via_graphviz(edge_index, edge_weight, path)
 
@@ -98,10 +100,15 @@ def _visualize_graph_via_networkx(
     edge_index: Tensor,
     edge_weight: Tensor,
     path: Optional[str] = None,
+    **kwargs,
 ) -> Any:
     import matplotlib.pyplot as plt
     import networkx as nx
 
+    node_label = kwargs['node_label']
+    color_dict = kwargs['color_dict']
+    target_node = kwargs['target_node']
+
     g = nx.DiGraph()
     node_size = 800
 
@@ -127,10 +134,26 @@ def _visualize_graph_via_networkx(
             ),
         )
 
+    node_color = ['white'] * len(g.nodes)
+    if node_label != None:
+        assert color_dict != None
+        node_color = []
+        for i, node_id in enumerate(list(g.nodes)):
+            node_color.append(color_dict[int(node_label[node_id])])
+
+    if target_node != None:
+        for i, node_id in enumerate(list(g.nodes)):
+            if node_id == target_node:
+                print("kylin")
+                node_color[i] = 'red'
+                break
+
     nodes = nx.draw_networkx_nodes(g, pos, node_size=node_size,
-                                   node_color='white', margins=0.1)
+                                   node_color=node_color, margins=0.1)
     nodes.set_edgecolor('black')
-    nx.draw_networkx_labels(g, pos, font_size=10)
+
+    if kwargs['draw_node_idx'] == True:
+        nx.draw_networkx_labels(g, pos, font_size=10)
 
     if path is not None:
         plt.savefig(path)