Graph Classification On Malnet Tiny
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Exphormer | 94.02±0.209 | Exphormer: Sparse Transformers for Graphs | |
| GatedGCN+ | 94.600±0.570 | Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence | |
| ESA (Edge set attention, no positional encodings) | 94.800±0.424 | An end-to-end attention-based approach for learning on graphs | |
| GPS | 93.36 ± 0.6 | Recipe for a General, Powerful, Scalable Graph Transformer |
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