diff --git a/lib/erosion_model.py b/lib/erosion_model.py
index 935737ac8d238632f302912a52d16f286cf576dc..6aa7e7da235e57df5b2282da923052370087645a 100644
--- a/lib/erosion_model.py
+++ b/lib/erosion_model.py
@@ -43,6 +43,7 @@ class ErosionModel():
         old_probs = dict(self.probs_df["hash_ p_{0}".format(self.nb_of_erosion - 1).split()].values)
 
         auc_sbm, auc_spatial = get_auc_heuristics(self.H, 60)
+        if VERBOSE:print(auc_sbm,auc_spatial)
         edges = get_all_possible_edges(self.H)
         if auc_sbm > auc_spatial:
             probs = stochastic_block_model(self.H, edges)
diff --git a/lib/link_prediction_eval.py b/lib/link_prediction_eval.py
index 5535a9c2b8a94bd9e4e9316239c472a7b5db3420..c022e833c213aa480effdc3956074d0a7f3f0289 100644
--- a/lib/link_prediction_eval.py
+++ b/lib/link_prediction_eval.py
@@ -7,7 +7,7 @@ from evalne.utils import preprocess as pp
 from .lambda_func import hash_func
 
 def get_auc_heuristics(G,timeout=60):
-    H, _ = pp.prep_graph(G.copy(),maincc=True)
+    H, _ = pp.prep_graph(G.copy(),maincc=True,relabel=False)
     traintest_split = LPEvalSplit()
     traintest_split.compute_splits(H, split_alg="spanning_tree", train_frac=0.90, fe_ratio=1)
     nee = LPEvaluator(traintest_split)