Node Classification On Amz Computers
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| CPF-ind-GAT | 85.5% | Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework | |
| DAGNN (Ours) | 84.5 ± 1.2 | Towards Deeper Graph Neural Networks | |
| NCGCN | 90.81 ± 0.46 | Clarify Confused Nodes via Separated Learning | |
| NCSAGE | 90.43 ± 0.72 | Clarify Confused Nodes via Separated Learning | |
| GLNN | 83.03± 1.87% | Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation |
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