Graph Regression on PCQM4Mv2-LSC

Metric: Test MAE (lower is better)

LeaderboardDataset

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#	Model↕	Test MAE▲	Extra Data	Paper	Date↕	Code
1	EGT + Triangular Attention	0.0683	No	Global Self-Attention as a Replacement for Graph...	2021-08-07	Code
2	TGT-At	0.0683	No	Triplet Interaction Improves Graph Transformers:...	2024-02-07	Code
3	Uni-Mol+	0.0705	No	Highly Accurate Quantum Chemical Property Predic...	2023-03-16	Code
4	Transformer-M	0.0782	No	One Transformer Can Understand Both 2D & 3D Mole...	2022-10-04	Code
5	GPTrans-L	0.0821	No	Graph Propagation Transformer for Graph Represen...	2023-05-19	Code
6	GPTrans-T	0.0842	No	Graph Propagation Transformer for Graph Represen...	2023-05-19	Code
7	GPS	0.0862	No	Recipe for a General, Powerful, Scalable Graph T...	2022-05-25	Code
8	EGT	0.0862	No	Global Self-Attention as a Replacement for Graph...	2021-08-07	Code
9	GRPE-Large	0.0876	No	GRPE: Relative Positional Encoding for Graph Tra...	2022-01-30	Code
10	TokenGT	0.0919	No	Pure Transformers are Powerful Graph Learners	2022-07-06	Code
11	GIN	0.1218	No	How Powerful are Graph Neural Networks?	2018-10-01	Code
12	GCN	0.1398	No	Semi-Supervised Classification with Graph Convol...	2016-09-09	Code
13	MLP-Fingerprint	0.176	No	OGB-LSC: A Large-Scale Challenge for Machine Lea...	2021-03-17	Code

#1EGT + Triangular AttentionSOTA
0.0683
Test MAE· 2021-08-07
Global Self-Attention as a Replacement for Graph Convolution Code
#2TGT-At
0.0683
Test MAE· 2024-02-07
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Code
#3Uni-Mol+
0.0705
Test MAE· 2023-03-16
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+Code
#4Transformer-M
0.0782
Test MAE· 2022-10-04
One Transformer Can Understand Both 2D & 3D Molecular Data Code
#5GPTrans-L
0.0821
Test MAE· 2023-05-19
Graph Propagation Transformer for Graph Representation Learning Code
#6GPTrans-T
0.0842
Test MAE· 2023-05-19
Graph Propagation Transformer for Graph Representation Learning Code
#7GPS
0.0862
Test MAE· 2022-05-25
Recipe for a General, Powerful, Scalable Graph Transformer Code
#8EGTSOTA
0.0862
Test MAE· 2021-08-07
Global Self-Attention as a Replacement for Graph Convolution Code
#9GRPE-Large
0.0876
Test MAE· 2022-01-30
GRPE: Relative Positional Encoding for Graph Transformer Code
#10TokenGT
0.0919
Test MAE· 2022-07-06
Pure Transformers are Powerful Graph Learners Code
#11GINSOTA
0.1218
Test MAE· 2018-10-01
How Powerful are Graph Neural Networks?Code
#12GCNSOTA
0.1398
Test MAE· 2016-09-09
Semi-Supervised Classification with Graph Convolutional Networks Code
#13MLP-Fingerprint
0.176
Test MAE· 2021-03-17
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs Code