Graph Classification On Hiv Fmri 77
Metriken
Accuracy
F1
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | F1 | Paper Title | Repository |
|---|---|---|---|---|
| WL | 44.2% | 27.2% | - | - |
| IsoNN-Fast | 70.5% | 69.9% | IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification | - |
| SDBN | 66.5% | 66.7% | Structural Deep Network Embedding | - |
| CNN | 59.3% | 66.3% | ImageNet Classification with Deep Convolutional Neural Networks | - |
| AE | 46.9% | 35.5% | - | - |
| GIN | 52.5% | 35.6% | How Powerful are Graph Neural Networks? | - |
| IsoNN | 73.4% | 72.2% | IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification | - |
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