Node Classification On Amazon Computers 1

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
GAT	94.09±0.37	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GCN	93.99±0.12	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GraphSAGE	93.25±0.14	Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(GCN-like P)	92.17±0.50	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(GAT-like P)	91.98±0.46	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(SAGE-like P)	91.85±0.39	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
CGT	91.45±0.58	Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
3ference	90.74%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
LinkDist	89.49%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDistMLP	89.44%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDist	89.42%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDistMLP	88.85%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

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Node Classification On Amazon Computers 1 | SOTA | HyperAI