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SOTA
Node Classification
Node Classification On Amazon Photo 1
Node Classification On Amazon Photo 1
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
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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