diff --git a/examples/pytorch/caregnn/main.py b/examples/pytorch/caregnn/main.py
index 7fb3fee5ef0f..1b202d81dd73 100644
--- a/examples/pytorch/caregnn/main.py
+++ b/examples/pytorch/caregnn/main.py
@@ -3,6 +3,7 @@
 import torch as th
 from model import CAREGNN
 import torch.optim as optim
+from torch.nn.functional import softmax
 from sklearn.metrics import recall_score, roc_auc_score
 
 from utils import EarlyStopping
@@ -70,13 +71,13 @@ def main(args):
                   args.sim_weight * loss_fn(logits_sim[train_idx], labels[train_idx])
 
         tr_recall = recall_score(labels[train_idx].cpu(), logits_gnn.data[train_idx].argmax(dim=1).cpu())
-        tr_auc = roc_auc_score(labels[train_idx].cpu(), logits_gnn.data[train_idx][:, 1].cpu())
+        tr_auc = roc_auc_score(labels[train_idx].cpu(), softmax(logits_gnn, dim=1).data[train_idx][:, 1].cpu())
 
         # validation
         val_loss = loss_fn(logits_gnn[val_idx], labels[val_idx]) + \
                    args.sim_weight * loss_fn(logits_sim[val_idx], labels[val_idx])
         val_recall = recall_score(labels[val_idx].cpu(), logits_gnn.data[val_idx].argmax(dim=1).cpu())
-        val_auc = roc_auc_score(labels[val_idx].cpu(), logits_gnn.data[val_idx][:, 1].cpu())
+        val_auc = roc_auc_score(labels[val_idx].cpu(), softmax(logits_gnn, dim=1).data[val_idx][:, 1].cpu())
 
         # backward
         optimizer.zero_grad()
@@ -106,7 +107,7 @@ def main(args):
     test_loss = loss_fn(logits_gnn[test_idx], labels[test_idx]) + \
                 args.sim_weight * loss_fn(logits_sim[test_idx], labels[test_idx])
     test_recall = recall_score(labels[test_idx].cpu(), logits_gnn[test_idx].argmax(dim=1).cpu())
-    test_auc = roc_auc_score(labels[test_idx].cpu(), logits_gnn.data[test_idx][:, 1].cpu())
+    test_auc = roc_auc_score(labels[test_idx].cpu(), softmax(logits_gnn, dim=1).data[test_idx][:, 1].cpu())
 
     print("Test Recall: {:.4f} AUC: {:.4f} Loss: {:.4f}".format(test_recall, test_auc, test_loss.item()))
 
diff --git a/examples/pytorch/caregnn/main_sampling.py b/examples/pytorch/caregnn/main_sampling.py
index 42e95e61dda7..54cee3937249 100644
--- a/examples/pytorch/caregnn/main_sampling.py
+++ b/examples/pytorch/caregnn/main_sampling.py
@@ -2,6 +2,7 @@
 import argparse
 import torch as th
 import torch.optim as optim
+from torch.nn.functional import softmax
 from sklearn.metrics import roc_auc_score, recall_score
 
 from utils import EarlyStopping
@@ -22,7 +23,7 @@ def evaluate(model, loss_fn, dataloader, device='cpu'):
         # compute loss
         loss += loss_fn(logits_gnn, label).item() + args.sim_weight * loss_fn(logits_sim, label).item()
         recall += recall_score(label.cpu(), logits_gnn.argmax(dim=1).detach().cpu())
-        auc += roc_auc_score(label.cpu(), logits_gnn[:, 1].detach().cpu())
+        auc += roc_auc_score(label.cpu(), softmax(logits_gnn, dim=1)[:, 1].detach().cpu())
         num_blocks += 1
 
     return recall / num_blocks, auc / num_blocks, loss / num_blocks
@@ -121,7 +122,7 @@ def main(args):
             blk_loss = loss_fn(logits_gnn, train_label) + args.sim_weight * loss_fn(logits_sim, train_label)
             tr_loss += blk_loss.item()
             tr_recall += recall_score(train_label.cpu(), logits_gnn.argmax(dim=1).detach().cpu())
-            tr_auc += roc_auc_score(train_label.cpu(), logits_gnn[:, 1].detach().cpu())
+            tr_auc += roc_auc_score(train_label.cpu(), softmax(logits_gnn, dim=1)[:, 1].detach().cpu())
             tr_blk += 1
 
             # backward