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SOTA
Node Classification
Node Classification On Amazon Computers 1
Node Classification On Amazon Computers 1
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
3ference
90.74%
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
GraphSAGE
93.25±0.14
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
CGT
91.45±0.58
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
GCN
93.99±0.12
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
CoLinkDistMLP
88.85%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
GAT
94.09±0.37
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(SAGE-like P)
91.85±0.39
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
CoLinkDist
89.42%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
GNNMoE(GAT-like P)
91.98±0.46
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
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
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