Node Classification on PPI

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Rank	Model	F1	Micro-F1	Micro F1	Macro-F1	Paper	Code	Year
1	g2-MLP	99.71	–	–	–	A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM	nnaakkaaii/g2-MLP	2022
2	GCNII*	99.56	–	–	–	Simple and Deep Graph Convolutional Networks	chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1	2020
3	GraphSAINT	99.50	–	–	–	GraphSAINT: Graph Sampling Based Inductive Learning Method	dmlc/dgl · GraphSAINT/GraphSAINT · thudm/graphmae2 · +5	2019
4	SGAS	99.46	–	–	–	SGAS: Sequential Greedy Architecture Search	lightaime/sgas	2019
5	DenseMRGCN-14	99.43	–	–	–	DeepGCNs: Making GCNs Go as Deep as CNNs	lightaime/deep_gcns_torch · lightaime/deep_gcns_torch · lightaime/deep_gcns_torch · +1	2019
6	ResMRGCN-28	99.41	–	–	–	DeepGCNs: Making GCNs Go as Deep as CNNs	lightaime/deep_gcns_torch · lightaime/deep_gcns_torch · lightaime/deep_gcns_torch · +1	2019
7	GraphStar	99.4	–	–	–	Graph Star Net for Generalized Multi-Task Learning	graph-star-team/graph_star	2019
8	GCN + SAF	99.38 ± 0.01%	–	–	–	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs	foisunt/fmms-in-gnns	2023
9	Cluster-GCN	99.36	–	–	–	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	google-research/google-research · dmlc/dgl · benedekrozemberczki/ClusterGCN · +3	2019
10	GAT + PGN	99.34 ± 0.02%	–	–	–	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs	foisunt/fmms-in-gnns	2023
11	DSGCN	99.09 ± 0.03	–	–	–	Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	balcilar/Spectral-Designed-Graph-Convolutions · sidneyarcidiacono/UnderstandingGCNs	2020
12	GaAN	98.7	–	–	–	GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs	jennyzhang0215/GaAN	2018
13	GraphNAS	98.6 ± 0.1	–	–	–	GraphNAS: Graph Neural Architecture Search with Reinforcement Learning	GraphNAS/GraphNAS-simple	2019
14	JK-LSTM	97.6	–	–	–	Representation Learning on Graphs with Jumping Knowledge Networks	dmlc/dgl · shinkyuy/representation_learning_on_graphs_with_jumping_knowledge_networks · mori97/JKNet-dgl · +2	2018
15	VQ-GNN (GAT)	97.37	–	–	–	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization	devnkong/VQ-GNN	2021
16	GAT	97.3	–	–	–	Graph Attention Networks	labmlai/annotated_deep_learning_paper_implementations · dmlc/dgl · dmlc/dgl · +90	2017
17	SIGN	96.50	–	–	–	SIGN: Scalable Inception Graph Neural Networks	dmlc/dgl · twitter-research/sign · facebookresearch/NARS · +2	2020
18	ClusterGCN	92.9	–	–	–	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	google-research/google-research · dmlc/dgl · benedekrozemberczki/ClusterGCN · +3	2019
19	LGCN	77.2	–	–	–	Large-Scale Learnable Graph Convolutional Networks	divelab/lgcn	2018
20	GRACE	66.2	66.2	–	–	Deep Graph Contrastive Representation Learning	dmlc/dgl · CRIPAC-DIG/GRACE · ycremar/DIG-SSL	2020