Graph Classification 벤치마크
Graph Classification on HIV dataset
ROC-AUC
- 2021-02-23 — GMT: ROC-AUC 77.56
- 2023-06-06 — CIN++: ROC-AUC 80.63
| Rank | Model | ROC-AUC | Accuracy | Paper | Code | Year |
|---|---|---|---|---|---|---|
| 1 | CIN++ | 80.63 | – | CIN++: Enhancing Topological Message Passing | twitter-research/cwn | 2023 |
| 2 | CIN++-small | 80.26 | – | CIN++: Enhancing Topological Message Passing | twitter-research/cwn | 2023 |
| 3 | GMT | 77.56 | – | Accurate Learning of Graph Representations with Graph Multiset Pooling | JinheonBaek/GMT | 2021 |
| 4 | TREE-G | – | 83.5 | TREE-G: Decision Trees Contesting Graph Neural Networks | mayabechlerspeicher/tree-g | 2022 |
| 5 | GAM | – | 74.79% | Graph Classification using Structural Attention | benedekrozemberczki/GAM | 2018 |
| 6 | CIN++ | 80.63 | – | CIN++: Enhancing Topological Message Passing | twitter-research/cwn | 2023 |
| 7 | CIN++-small | 80.26 | – | CIN++: Enhancing Topological Message Passing | twitter-research/cwn | 2023 |
| 8 | GMT | 77.56 | – | Accurate Learning of Graph Representations with Graph Multiset Pooling | JinheonBaek/GMT | 2021 |
| 9 | TREE-G | – | 83.5 | TREE-G: Decision Trees Contesting Graph Neural Networks | mayabechlerspeicher/tree-g | 2022 |
| 10 | GAM | – | 74.79% | Graph Classification using Structural Attention | benedekrozemberczki/GAM | 2018 |