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Node Classification On Amazon Computers 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
3ference90.74%Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
GraphSAGE93.25±0.14Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
CGT91.45±0.58Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
GCN93.99±0.12Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(GCN-like P)92.17±0.50Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
CoLinkDistMLP88.85%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
GAT94.09±0.37Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(SAGE-like P)91.85±0.39Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
CoLinkDist89.42%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
GNNMoE(GAT-like P)91.98±0.46Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
LinkDist89.49%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDistMLP89.44%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
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