Graph Classification on COLLAB

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Accuracy

Rank	Model	Accuracy	Accuracy (10-fold)	Extra Training Data	Paper	Code	Year
1	U2GNN (Unsupervised)	95.62%	–		Universal Graph Transformer Self-Attention Networks	daiquocnguyen/Graph-Transformer	2019
2	TFGW ADJ (L=2)	84.3%	–		Template based Graph Neural Network with Optimal Transport Distances	cedricvincentcuaz/TFGW	2022
3	DUGNN	84.20%	–	✓	Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning	vermaMachineLearning/Universal-Graph-Embedding-Neural-Network	2019
4	G_DenseNet	83.16%	–		When Work Matters: Transforming Classical Network Structures to Graph CNN		2018
5	GFN	81.50%	–		Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification	chentingpc/gfn	2019
6	PPGN	81.38%	–		Provably Powerful Graph Networks	hadarser/ProvablyPowerfulGraphNetworks_torch · hadarser/ProvablyPowerfulGraphNetworks	2019
7	GFN-light	81.34%	–		Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification	chentingpc/gfn	2019
8	FactorGCN	81.2%	81.2%		Factorizable Graph Convolutional Networks	ihollywhy/FactorGCN.PyTorch	2020
9	GMT	80.74%	–		Accurate Learning of Graph Representations with Graph Multiset Pooling	JinheonBaek/GMT	2021
10	sGIN	80.71%	–		Mutual Information Maximization in Graph Neural Networks	CODE-SUBMIT/Graph_Neighborhood_1 · CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network	2019
11	GCN	80.6%	–		Fast Graph Representation Learning with PyTorch Geometric	rusty1s/pytorch_geometric · leojklarner/gauche · ncfrey/litmatter · +3	2019
12	Self-supervised GraphMAE	80.32%	–		GraphMAE: Self-Supervised Masked Graph Autoencoders	thudm/graphmae · thudm/graphmae2 · wehos/cellt	2022
13	GIN-0	80.2%	–		How Powerful are Graph Neural Networks?	dmlc/dgl · dmlc/dgl · weihua916/powerful-gnns · +16	2018
14	WEGL	79.8%	–		Wasserstein Embedding for Graph Learning	navid-naderi/WEGL	2020
15	MEWISPool	79.66%	–		Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks	mewispool/mewispool	2021
16	CapsGNN	79.62%	–		Capsule Graph Neural Network	benedekrozemberczki/CapsGNN	2019
17	Local Topological Profile (LTP)	79.4 ± 2.5	79.4 ± 2.5		Strengthening structural baselines for graph classification using Local Topological Profile	j-adamczyk/ltp	2023
18	NDP	79.1%	–		Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling	danielegrattarola/decimation-pooling	2019
19	SEG-BERT	78.42%	–		Segmented Graph-Bert for Graph Instance Modeling	jwzhanggy/SEG-BERT	2020
20	U2GNN	77.84%	–		Universal Graph Transformer Self-Attention Networks	daiquocnguyen/Graph-Transformer	2019