diff --git a/metainfo/explainers_init_parameters.json b/metainfo/explainers_init_parameters.json
index 66596f8..a4f93be 100644
--- a/metainfo/explainers_init_parameters.json
+++ b/metainfo/explainers_init_parameters.json
@@ -20,8 +20,8 @@
  "GNNExplainer(torch-geom)": {
   "epochs":  ["Epochs","int",100,{"min": 1},"The number of epochs to train"],
   "lr": ["Learn rate","float",0.01,{"min": 0, "step": 0.0001},"The learning rate to apply"],
-  "node_mask_type": ["Node mask","string","object",["None","object","common_attributes","attributes"],"The type of mask to apply on nodes"],
-  "edge_mask_type": ["Edge mask","string","object",["None","object","common_attributes","attributes"],"The type of mask to apply on edges"],
+  "node_mask_type": ["Node mask","string","object",["None","object","common_attributes"],"The type of mask to apply on nodes"],
+  "edge_mask_type": ["Edge mask","string","object",["None","object"],"The type of mask to apply on edges"],
   "mode": ["Mode","string","multiclass_classification",["binary_classification","multiclass_classification","regression"],"The mode of the model"],
   "return_type": ["Model return","string","log_probs",["raw","prob","log_probs"],"Denotes the type of output from model. Valid inputs are 'log_probs' (the model returns the logarithm of probabilities), 'prob' (the model returns probabilities), 'raw' (the model returns raw scores)"],
   "edge_size": ["edge_size","float",0.005,{"min": 0, "step": 0.001},""],
diff --git a/src/explainers/GNNExplainer/torch_geom_our/out.py b/src/explainers/GNNExplainer/torch_geom_our/out.py
index e0a10ce..fa6497d 100644
--- a/src/explainers/GNNExplainer/torch_geom_our/out.py
+++ b/src/explainers/GNNExplainer/torch_geom_our/out.py
@@ -111,13 +111,14 @@ def _finalize(self):
 
         self.explanation = AttributionExplanation(
             local=mode,
-            edges="continuous" if edge_mask is not None else False,
-            features="continuous" if node_mask is not None else False)
+            edges="continuous" if self.edge_mask_type=="object" else False,
+            nodes="continuous" if self.node_mask_type=="object" else False,
+            features="continuous" if self.node_mask_type=="common_attributes" else False)
 
         important_edges = {}
         important_nodes = {}
+        important_features = {}
 
-        # TODO What if edge_mask_type or node_mask_type is None, common_attributes, attributes?
         if self.edge_mask_type is not None and self.node_mask_type is not None:
 
             # Multi graphs check is not needed: the explanation format for
@@ -136,21 +137,34 @@ def _finalize(self):
                     important_edges[f"{edge[0]},{edge[1]}"] = format(imp, '.4f')
 
             # Nodes
-            num_nodes = node_mask.size(0)
-            assert num_nodes == self.x.size(0)
+            if self.node_mask_type=="object":
+                num_nodes = node_mask.size(0)
+                assert num_nodes == self.x.size(0)
 
-            for i in range(num_nodes):
-                imp = float(node_mask[i][0])
-                if not imp < eps:
-                    important_nodes[i] = format(imp, '.4f')
+                for i in range(num_nodes):
+                    imp = float(node_mask[i][0])
+                    if not imp < eps:
+                        important_nodes[i] = format(imp, '.4f')
+
+            # Features
+            elif self.node_mask_type=="common_attributes":
+                num_features = node_mask.size(1)
+                assert num_features == self.x.size(1)
+
+                for i in range(num_features):
+                    imp = float(node_mask[0][i])
+                    if not imp < eps:
+                        important_features[i] = format(imp, '.4f')
 
         if self.gen_dataset.is_multi():
             important_edges = {self.graph_idx: important_edges}
             important_nodes = {self.graph_idx: important_nodes}
+            important_features = {self.graph_idx: important_features}
 
         # TODO Write functions with output threshold
         self.explanation.add_edges(important_edges)
         self.explanation.add_nodes(important_nodes)
+        self.explanation.add_features(important_features)
 
         # print(important_edges)
         # print(important_nodes)