diff --git a/explaining_framework/utils/explaining/outline.py b/explaining_framework/utils/explaining/outline.py
index 590f4c4..70bd2da 100644
--- a/explaining_framework/utils/explaining/outline.py
+++ b/explaining_framework/utils/explaining/outline.py
@@ -6,23 +6,10 @@ import os
 from typing import Any
 
 from eixgnn.eixgnn import EiXGNN
-from scgnn.scgnn import SCGNN
-from torch_geometric import seed_everything
-from torch_geometric.data import Batch, Data
-from torch_geometric.data.makedirs import makedirs
-from torch_geometric.explain import Explainer
-from torch_geometric.explain.config import ThresholdConfig
-from torch_geometric.explain.explanation import Explanation
-from torch_geometric.graphgym.config import cfg
-from torch_geometric.graphgym.loader import create_dataset, create_dataset2
-from torch_geometric.graphgym.model_builder import cfg, create_model
-from torch_geometric.graphgym.utils.device import auto_select_device
-from torch_geometric.loader.dataloader import DataLoader
-from yacs.config import CfgNode as CN
-
 from explaining_framework.config.explainer_config.eixgnn_config import \
     eixgnn_cfg
 from explaining_framework.config.explainer_config.scgnn_config import scgnn_cfg
+from explaining_framework.config.explainer_config.xgwt_config import xgwt_cfg
 from explaining_framework.config.explaining_config import explaining_cfg
 from explaining_framework.explainers.wrappers.from_captum import CaptumWrapper
 from explaining_framework.explainers.wrappers.from_graphxai import \
@@ -45,6 +32,20 @@ from explaining_framework.utils.io import (dump_cfg, is_exists,
                                            obj_config_to_str, read_json,
                                            set_printing, write_json,
                                            write_yaml)
+from scgnn.scgnn import SCGNN
+from torch_geometric import seed_everything
+from torch_geometric.data import Batch, Data
+from torch_geometric.data.makedirs import makedirs
+from torch_geometric.explain import Explainer
+from torch_geometric.explain.config import ThresholdConfig
+from torch_geometric.explain.explanation import Explanation
+from torch_geometric.graphgym.config import cfg
+from torch_geometric.graphgym.loader import create_dataset, create_dataset2
+from torch_geometric.graphgym.model_builder import cfg, create_model
+from torch_geometric.graphgym.utils.device import auto_select_device
+from torch_geometric.loader.dataloader import DataLoader
+from xgwt.xgwt import XGWT
+from yacs.config import CfgNode as CN
 
 all__captum = [
     "LRP",
@@ -81,7 +82,7 @@ all__pyg = [
     # "GNNExplainer",
 ]
 
-all__own = ["EIXGNN", "SCGNN"]
+all__own = ["EIXGNN", "SCGNN", "XGWT"]
 
 all_fidelity = [
     "fidelity_plus",
@@ -218,6 +219,8 @@ class ExplainingOutline(object):
                     self.explainer_cfg = copy.copy(eixgnn_cfg)
                 elif self.explaining_cfg.explainer.name == "SCGNN":
                     self.explainer_cfg = copy.copy(scgnn_cfg)
+                elif self.explaining_cfg.explainer.name == "XGWT":
+                    self.explainer_cfg = copy.copy(xgwt_cfg)
                 else:
                     self.explainer_cfg = CN()
             else:
@@ -227,6 +230,9 @@ class ExplainingOutline(object):
                 elif self.explaining_cfg.explainer.name == "SCGNN":
                     scgnn_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
                     self.explainer_cfg = scgnn_cfg
+                elif self.explaining_cfg.explainer.name == "XGWT":
+                    xgwt_cfg.merge_from_file(self.explaining_cfg.explainer.cfg)
+                    self.explainer_cfg = xgwt_cfg
 
     def load_model(self):
         if self.cfg is None:
@@ -290,6 +296,22 @@ class ExplainingOutline(object):
                     signal_similarity=self.explainer_cfg.signal_similarity,
                     shap_val_approx=self.explainer_cfg.shapley_value_approx,
                 )
+            elif name == "XGWT":
+                explaining_algorithm = XGWT(
+                    wav_approx=self.explainer_cfg.wav_approx,
+                    wav_passband=self.explainer_cfg.wav_passband,
+                    wav_normalization=self.explainer_cfg.wav_normalization,
+                    num_candidates=self.explainer_cfg.num_candidates,
+                    num_samples=self.explainer_cfg.num_samples,
+                    c_procedure=self.explainer_cfg.c_procedure,
+                    pred_thres_strat=self.explainer_cfg.pred_thres_strat,
+                    CI_threshold=self.explainer_cfg.CI_threshold,
+                    mixing=self.explainer_cfg.mixing,
+                    pred_thres=self.explainer_cfg.pred_thres,
+                    incl_prob=self.explainer_cfg.incl_prob,
+                    top_k=self.explainer_cfg.top_k,
+                    scales=self.explainer_cfg.scales,
+                )
             elif name == "SCGNN":
                 explaining_algorithm = SCGNN(
                     depth=self.explainer_cfg.depth,