| Paper | Code | Accuracy | ModelName | ReleaseDate |
|---|---|---|---|---|
| How Powerful are Graph Neural Networks? | ✓ Link | 57.5% | GIN-0 | 2018-10-01 |
| Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation | ✓ Link | 57.22% | GAT-GC (f-Scaled) | 2019-07-04 |
| Wasserstein Embedding for Graph Learning | ✓ Link | 55.1% | WEGL | 2020-06-16 |
| Capsule Graph Neural Network | ✓ Link | 52.88% | CapsGNN | 2019-05-01 |
| Graph Classification with 2D Convolutional Neural Networks | 52.11% | 2D CNN | 2017-07-29 | |
| Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification | ✓ Link | 49.75% | GFN-light | 2019-05-11 |
| Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification | ✓ Link | 49.43% | GFN | 2019-05-11 |
| Deep Graph Kernels | 41.27% | DGK | 2015-08-10 |