PyG Benchmark

Graph Property Prediction

Dataset	ogbg-molhiv		ogbg-ppa
Trick	GCN	GIN	GCN	GIN
—	0.7690 ± 0.0053	0.7778 ± 0.0130	0.6787 ± 0.0091	0.6833 ± 0.0087
+Virtual Node	0.7581 ± 0.0135	0.7713 ± 0.0036	0.6747 ± 0.0060	0.6901 ± 0.0277
+FLAG	0.7627 ± 0.0124	0.7764 ± 0.0083
+Random Feature	0.7743 ± 0.0134	0.7692 ± 0.0065
Random Forest + Fingerprint	0.8218 ± 0.0022

Run the baseline code: python graph_pred.py --model gin/gcn

Node Property Prediction

Dataset	ogbn-arxiv
Trick	GCN	SAGE
—	0.7167 ± 0.0022	0.7167 ± 0.0025
+FLAG	0.7187 ± 0.0020	0.7206 ± 0.0013
+Label Propagation	0.7212 ± 0.0006	0.7197 ± 0.0020
+Correct & Smooth	0.7220 ± 0.0037	0.7264 ± 0.0004

Run the baseline code: python node_pred.py --model gcn/sage

Link Property Prediction

Dataset	ogbn-collab
Trick	GCN	SAGE
—	0.4763 ± 0.0279	0.5171 ± 0.0100
+Common Neighbors	0.5422 ± 0.0023	0.5368 ± 0.0020
+Resource Allocation	0.5033 ± 0.0142	0.5093 ± 0.0055
+Adamic Adar	0.5345 ± 0.0023	0.5376 ± 0.0010
+AnchorDistance	0.4605 ± 0.0120	0.4553 ± 0.0237

Run the baseline code: python link_pred.py --model gcn/sage