Graph Property Prediction On Ogbg Ppa

Metrics

Ext. data

Number of params

Test Accuracy

Validation Accuracy

Results

Performance results of various models on this benchmark

					Paper Title
ExpC	No	1369397	0.7976 ± 0.0072	0.7518 ± 0.0080	Breaking the Expressive Bottlenecks of Graph Neural Networks
GCN	No	479437	0.6839 ± 0.0084	0.6497 ± 0.0034	Semi-Supervised Classification with Graph Convolutional Networks
GatedGCN+	No	5547557	0.8258 ± 0.0055	0.7815 ± 0.0043	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GCN+	No	5549605	0.8077 ± 0.0041	0.7586 ± 0.0032	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GC-T+MCL(6.0)	No	4006704	0.7432 ± 0.0033	0.6989 ± 0.0037	-
DeeperGCN	No	2336421	0.7712 ± 0.0071	0.7313 ± 0.0078	DeeperGCN: All You Need to Train Deeper GCNs
GIN+virtual node	No	3288042	0.7037 ± 0.0107	0.6678 ± 0.0105	How Powerful are Graph Neural Networks?
DeeperGCN+FLAG	No	2336421	0.7752 ± 0.0069	0.7484 ± 0.0052	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+FLAG	No	1836942	0.6905 ± 0.0092	0.6465 ± 0.0070	Robust Optimization as Data Augmentation for Large-scale Graphs
PAS+F2GNN	No	16346166	0.8201 ± 0.0019	0.7720 ± 0.0023	-
GIN+	No	8173605	0.8107 ± 0.0053	0.7786 ± 0.0095	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
PAS	No	3717160	0.7828 ± 0.0024	0.7523 ± 0.0028	-
GCN+virtual node+FLAG	No	1930537	0.6944 ± 0.0052	0.6638 ± 0.0055	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+virtual node+FLAG	No	3288042	0.7245 ± 0.0114	0.6789 ± 0.0079	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN	No	1836942	0.6892 ± 0.0100	0.6562 ± 0.0107	How Powerful are Graph Neural Networks?
ExpC*+bag of tricks	No	3758642	0.8140 ± 0.0028	0.7811 ± 0.0012	-
GCN+virtual node	No	1930537	0.6857 ± 0.0061	0.6511 ± 0.0048	Semi-Supervised Classification with Graph Convolutional Networks
GPS	No	3434533	0.8015	0.7556 ± 0.0027	Recipe for a General, Powerful, Scalable Graph Transformer

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Graph Property Prediction On Ogbg Ppa

Metrics

Ext. data

Number of params

Test Accuracy

Validation Accuracy

Results

Performance results of various models on this benchmark

					Paper Title
ExpC	No	1369397	0.7976 ± 0.0072	0.7518 ± 0.0080	Breaking the Expressive Bottlenecks of Graph Neural Networks
GCN	No	479437	0.6839 ± 0.0084	0.6497 ± 0.0034	Semi-Supervised Classification with Graph Convolutional Networks
GatedGCN+	No	5547557	0.8258 ± 0.0055	0.7815 ± 0.0043	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GCN+	No	5549605	0.8077 ± 0.0041	0.7586 ± 0.0032	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GC-T+MCL(6.0)	No	4006704	0.7432 ± 0.0033	0.6989 ± 0.0037	-
DeeperGCN	No	2336421	0.7712 ± 0.0071	0.7313 ± 0.0078	DeeperGCN: All You Need to Train Deeper GCNs
GIN+virtual node	No	3288042	0.7037 ± 0.0107	0.6678 ± 0.0105	How Powerful are Graph Neural Networks?
DeeperGCN+FLAG	No	2336421	0.7752 ± 0.0069	0.7484 ± 0.0052	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+FLAG	No	1836942	0.6905 ± 0.0092	0.6465 ± 0.0070	Robust Optimization as Data Augmentation for Large-scale Graphs
PAS+F2GNN	No	16346166	0.8201 ± 0.0019	0.7720 ± 0.0023	-
GIN+	No	8173605	0.8107 ± 0.0053	0.7786 ± 0.0095	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
PAS	No	3717160	0.7828 ± 0.0024	0.7523 ± 0.0028	-
GCN+virtual node+FLAG	No	1930537	0.6944 ± 0.0052	0.6638 ± 0.0055	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN+virtual node+FLAG	No	3288042	0.7245 ± 0.0114	0.6789 ± 0.0079	Robust Optimization as Data Augmentation for Large-scale Graphs
GIN	No	1836942	0.6892 ± 0.0100	0.6562 ± 0.0107	How Powerful are Graph Neural Networks?
ExpC*+bag of tricks	No	3758642	0.8140 ± 0.0028	0.7811 ± 0.0012	-
GCN+virtual node	No	1930537	0.6857 ± 0.0061	0.6511 ± 0.0048	Semi-Supervised Classification with Graph Convolutional Networks
GPS	No	3434533	0.8015	0.7556 ± 0.0027	Recipe for a General, Powerful, Scalable Graph Transformer

0 of 18 row(s) selected.

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Graph Property Prediction On Ogbg Ppa

Metrics

Results

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Graph Property Prediction On Ogbg Ppa

Metrics

Results