Graph Property Prediction On Ogbg Code2
Métriques
Ext. data
Number of params
Test F1 score
Validation F1 score
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Ext. data | Number of params | Test F1 score | Validation F1 score | Paper Title | Repository |
|---|---|---|---|---|---|---|
| GCN | No | 11033210 | 0.1507 ± 0.0018 | 0.1399 ± 0.0017 | Semi-Supervised Classification with Graph Convolutional Networks | |
| GAT | No | 11030210 | 0.1569 ± 0.0010 | 0.1442 ± 0.0017 | Graph Attention Networks | |
| EGC-M (No Edge Features) | No | 10986002 | 0.1595 ± 0.0019 | 0.1464 ± 0.0021 | Do We Need Anisotropic Graph Neural Networks? | |
| SAT | No | 15734000 | 0.1937 ± 0.0028 | 0.1773 ± 0.0023 | Structure-Aware Transformer for Graph Representation Learning | |
| DAGNN | No | 35246814 | 0.1751 ± 0.0049 | 0.1607 ± 0.0040 | - | - |
| GMAN+bag of tricks | No | 63684290 | 0.1770 ± 0.0012 | 0.1631 ± 0.0090 | - | - |
| MPNN-Max (No Edge Features) | No | 10971506 | 0.1552 ± 0.0022 | 0.1441 ± 0.0016 | Do We Need Anisotropic Graph Neural Networks? | |
| GPS | No | 12454066 | 0.1894 | 0.1739 ± 0.001 | Recipe for a General, Powerful, Scalable Graph Transformer | |
| DiffPool w/ graphSAGE | No | 10095826 | 0.1401 ± 0.0012 | 0.1405 ± 0.0012 | Hierarchical Graph Representation Learning with Differentiable Pooling | |
| SAT++ with Magnetic Laplacian | No | 14378069 | 0.2222 ± 0.0010 | 0.2044 ± 0.0020 | Transformers Meet Directed Graphs | |
| GraphTrans (GCN-Virtual) | No | 9053246 | 0.1830 ± 0.0024 | 0.1661 ± 0.0012 | - | - |
| PNA (No Edge Features) | No | 10992050 | 0.1570 ± 0.0032 | 0.1453 ± 0.0025 | Do We Need Anisotropic Graph Neural Networks? | |
| GIN+virtual node | No | 13841815 | 0.1581 ± 0.0026 | 0.1439 ± 0.0020 | How Powerful are Graph Neural Networks? | |
| SAT++ with Magnetic Laplacian | No | 14378069 | 0.2222 ± 0.0032 | 0.2044 ± 0.0020 | - | - |
| GIN | No | 12390715 | 0.1495 ± 0.0023 | 0.1376 ± 0.0016 | How Powerful are Graph Neural Networks? | |
| EGC-S (No Edge Features) | No | 11156530 | 0.1528 ± 0.0025 | 0.1427 ± 0.0020 | Do We Need Anisotropic Graph Neural Networks? | |
| DAGformer | No | 14952882 | 0.2018 ± 0.0021 | 0.1846 ± 0.0010 | - | - |
| GraphTrans (GCN) | No | 7563746 | 0.1751 ± 0.0015 | 0.1599 ± 0.0009 | - | - |
| GCN+virtual node | No | 12484310 | 0.1595 ± 0.0018 | 0.1461 ± 0.0013 | Semi-Supervised Classification with Graph Convolutional Networks | |
| DAGNN | - | - | 0.1751 ± 0.0049 | 0.1607 ± 0.0040 | Directed Acyclic Graph Neural Networks |
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