Graph Classification On Malnet Tiny
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | 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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