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HyperAI超神经

Node Classification On Amazon Photo 1

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

		Paper Title	Repository
GraphSAGE	96.78 ± 0.23	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GAT	96.60 ± 0.33	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GCN	96.10 ± 0.46	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(GCN-like P)	95.81±0.41	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(GAT-like P)	95.71±0.37	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(SAGE-like P)	95.46±0.24	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
3ference	95.05%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	-
CoLinkDist	94.36%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDistMLP	94.12%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDistMLP	93.83%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDist	93.75%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

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Node Classification On Amazon Photo 1 | SOTA | HyperAI超神经