update README.md

README.md

@@ -32,7 +32,7 @@ The performance of "**PLNLP (val as input)**"  on the leader board can be reprod
 
 Furthermore, we sample high-order pairs with random walk and employ them as a kind of data augmentation. This augmentation method improves the performance significantly. To reproduce the performance of "**PLNLP (random walk aug.)**" on the leader board, you can use the following command:
 
-    python main.py --data_name=ogbl-collab  --predictor=DOT --use_valedges_as_input=True --year=2010 --train_on_subgraph=True --epochs=800 --eval_last_best=True --dropout=0.3 --gnn_num_layers=1 --grad_clip_norm=1 --use_lr_decay=True --random_walk_augment=True --walk_length=10
+    python main.py --data_name=ogbl-collab  --predictor=DOT --use_valedges_as_input=True --year=2010 --train_on_subgraph=True --epochs=800 --eval_last_best=True --dropout=0.3 --gnn_num_layers=1 --grad_clip_norm=1 --use_lr_decay=True --random_walk_augment=True --walk_length=10 --loss_func=WeightedHingeAUC
 
 
 ### ogbl-citation2: