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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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Node Classification On Amazon Computers 1 | SOTA | HyperAI