Graph Property Prediction On Ogbg Molpcba

評価指標

Ext. data

Number of params

Test AP

Validation AP

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名	Ext. data	Number of params	Test AP	Validation AP	Paper Title	Repository
GINE+ w/ APPNP	No	6147029	0.2979 ± 0.0030	0.3126 ± 0.0023	Graph convolutions that can finally model local structure	-
CRaWl	No	6115728	0.2986 ± 0.0025	0.3075 ± 0.0020	Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing	-
GIN+virtual node	No	3374533	0.2703 ± 0.0023	0.2798 ± 0.0025	How Powerful are Graph Neural Networks?	-
GatedGCN+	No	6016860	0.2981 ± 0.0024	0.3011 ± 0.0037	Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence	-
GCN	No	565928	0.2020 ± 0.0024	0.2059 ± 0.0033	Semi-Supervised Classification with Graph Convolutional Networks	-
PHC-GNN	No	1690328	0.2947 ± 0.0026	0.3068 ± 0.0025	Parameterized Hypercomplex Graph Neural Networks for Graph Classification	-
GCN+virtual node+FLAG	No	2017028	0.2483 ± 0.0037	0.2556 ± 0.0040	Robust Optimization as Data Augmentation for Large-scale Graphs	-
HyperFusion	No	10887085	0.3204 ± 0.0001	0.3353 ± 0.0002	-	-
GIN-AK	No	3081029	0.2930 ± 0.0044	0.3047 ± 0.0007	From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness	-
TGT-Ag+TGT-At-DP	Yes	47000000	0.3167 ± 0.0031	-	Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers	-
Nested GIN+virtual node (ensemble)	No	44187480	0.3007 ± 0.0037	0.3059 ± 0.0056	Nested Graph Neural Networks	-
GCN+virtual node	No	2017028	0.2424 ± 0.0034	0.2495 ± 0.0042	Semi-Supervised Classification with Graph Convolutional Networks	-
Nested GIN+virtual node (ens)	-	-	0.3007 ± 0.0037	0.3059 ± 0.0056	Nested Graph Neural Networks	-
Graphormer	-	119529664	0.3140 ± 0.0032	0.3227 ± 0.0024	Do Transformers Really Perform Bad for Graph Representation?	-
PDF	No	3842048	0.3031 ± 0.0026	0.3115 ± 0.0020	Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering	-
DGN	No	6732696	0.2885 ± 0.0030	0.2970 ± 0.0021	Directional Graph Networks	-
GPS	No	9744496	0.2907	0.3015 ± 0.0038	Recipe for a General, Powerful, Scalable Graph Transformer	-
Graphormer (pre-trained on PCQM4M)	Yes	119529664	0.3140 ± 0.0032	0.3227 ± 0.0024	Do Transformers Really Perform Bad for Graph Representation?	-
PNA	No	6550839	0.2838 ± 0.0035	0.2926 ± 0.0026	Principal Neighbourhood Aggregation for Graph Nets	-
RandomGIN-vn+FLAG	No	5572026	0.2881 ± 0.0028	0.3035 ± 0.0047	RAN-GNNs: breaking the capacity limits of graph neural networks	-

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