Node Classification On Amz Comp
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Graph InfoClust (GIC) | 81.5 ± 1.0 | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning | |
| GCN (Heat Diffusion) | 86.77% | Diffusion Improves Graph Learning | |
| GraphSAGE | 84.79% | Half-Hop: A graph upsampling approach for slowing down message passing | |
| GCN | 90.22% | Half-Hop: A graph upsampling approach for slowing down message passing | |
| HH-GCN | 90.92% | Half-Hop: A graph upsampling approach for slowing down message passing | |
| HH-GraphSAGE | 86.6% | Half-Hop: A graph upsampling approach for slowing down message passing | |
| SIGN | 85.93 ± 1.21 | SIGN: Scalable Inception Graph Neural Networks |
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