Node Classification on PATTERN 100k

Metric: Accuracy (%) (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy (%)▼	Extra Data	Paper	Date↕	Code
1	EGT	86.816	No	Global Self-Attention as a Replacement for Graph...	2021-08-07	Code
2	DGN	86.68	No	Directional Graph Networks	2020-10-06	Code
3	PNA	86.567	No	Principal Neighbourhood Aggregation for Graph Nets	2020-04-12	Code
4	GIN	85.59	No	How Powerful are Graph Neural Networks?	2018-10-01	Code
5	MoNet	85.482	No	Geometric deep learning on graphs and manifolds ...	2016-11-25	Code
6	GatedGCN	84.48	No	Residual Gated Graph ConvNets	2017-11-20	Code
7	GAT	75.824	No	Graph Attention Networks	2017-10-30	Code
8	GraphSage	50.516	No	Inductive Representation Learning on Large Graphs	2017-06-07	Code

#1EGTSOTA
86.816
Accuracy (%)· 2021-08-07
Global Self-Attention as a Replacement for Graph Convolution Code
#2DGNSOTA
86.68
Accuracy (%)· 2020-10-06
Directional Graph Networks Code
#3PNASOTA
86.567
Accuracy (%)· 2020-04-12
Principal Neighbourhood Aggregation for Graph Nets Code
#4GINSOTA
85.59
Accuracy (%)· 2018-10-01
How Powerful are Graph Neural Networks?Code
#5MoNetSOTA
85.482
Accuracy (%)· 2016-11-25
Geometric deep learning on graphs and manifolds using mixture model CNNs Code
#6GatedGCN
84.48
Accuracy (%)· 2017-11-20
Residual Gated Graph ConvNets Code
#7GAT
75.824
Accuracy (%)· 2017-10-30
Graph Attention Networks Code
#8GraphSage
50.516
Accuracy (%)· 2017-06-07
Inductive Representation Learning on Large Graphs Code