Graph Classification On Malnet Tiny
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Exphormer | 94.02±0.209 | Exphormer: Sparse Transformers for Graphs | |
| GatedGCN+ | 94.600±0.570 | Unlocking the Potential of Classic GNNs 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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