Graph Classification on PROTEINS

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Accuracy

Rank	Model	Accuracy	Accuracy (10 fold)	Inference Time (ms)	Extra Training Data	Paper	Code	Year
1	HGP-SL	84.91	–	–		Hierarchical Graph Pooling with Structure Learning	dmlc/dgl · cszhangzhen/HGP-SL · gsl-benchmark/gslb	2019
2	rLap (unsupervised)	84.3	–	–		Randomized Schur Complement Views for Graph Contrastive Learning	kvignesh1420/rlap	2023
3	TFGW ADJ (L=2)	82.9	–	–		Template based Graph Neural Network with Optimal Transport Distances	cedricvincentcuaz/TFGW	2022
4	ESA (Edge set attention, no positional encodings)	82.679±0.799	–	–		An end-to-end attention-based approach for learning on graphs	davidbuterez/edge-set-attention	2024
5	FIT-GNN	82.1	–	0.0016		FIT-GNN: Faster Inference Time for GNNs Using Coarsening	Roy-Shubhajit/FIT-GNN	2024
6	DUGNN	81.70%	–	–	✓	Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning	vermaMachineLearning/Universal-Graph-Embedding-Neural-Network	2019
7	MEWISPool	80.71%	–	–		Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks	mewispool/mewispool	2021
8	CIN++	80.5	–	–		CIN++: Enhancing Topological Message Passing	twitter-research/cwn	2023
9	SAEPool	80.36%	–	–		Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization		2022
9	MAGPool	80.36%	–	–		Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective	p-haddadian/MAGPool	2024
11	UGT	80.12 ±0.32	–	–		Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity	nslab-cuk/unified-graph-transformer · nslab-cuk/community-aware-graph-transformer · nslab-cuk/literalkg	2023
12	U2GNN (Unsupervised)	80.01%	–	–		Universal Graph Transformer Self-Attention Networks	daiquocnguyen/Graph-Transformer	2019
13	sGIN	78.97%	–	–		Mutual Information Maximization in Graph Neural Networks	CODE-SUBMIT/Graph_Neighborhood_1 · CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network	2019
14	QS-CNNs (Quantum Walk)	78.80%	–	–		Quantum-based subgraph convolutional neural networks		2019
14	WKPI-kmeans	78.8%	–	–		Learning metrics for persistence-based summaries and applications for graph classification	topology474/WKPI	2019
14	PIN	78.8%	–	–		Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes		2023
17	hGANet	78.65%	–	–		Graph Representation Learning via Hard and Channel-Wise Attention Networks	dmlc/dgl	2019
18	U2GNN	78.53%	–	–		Universal Graph Transformer Self-Attention Networks	daiquocnguyen/Graph-Transformer	2019
19	DS-CNNs (Random Walk)	78.35%	–	–		Quantum-based subgraph convolutional neural networks		2019
20	cGANet	78.23%	–	–		Graph Representation Learning via Hard and Channel-Wise Attention Networks	dmlc/dgl	2019