Metric: Number of params (higher is better)
| # | Model↕ | Number of params▼ | Extra Data | Paper | Date↕ | Code |
|---|---|---|---|---|---|---|
| 1 | GMAN+bag of tricks | 63684290 | No | - | - | - |
| 2 | DAGNN | 35246814 | No | - | - | - |
| 3 | SAT | 15734000 | No | Structure-Aware Transformer for Graph Representa... | 2022-02-07 | Code |
| 4 | DAGformer | 14952882 | No | Transformers over Directed Acyclic Graphs | 2022-10-24 | Code |
| 5 | SAT++ with Magnetic Laplacian | 14378069 | No | Transformers Meet Directed Graphs | 2023-01-31 | Code |
| 6 | SAT++ with Magnetic Laplacian | 14378069 | No | Transformers Meet Directed Graphs | 2023-01-31 | Code |
| 7 | GIN+virtual node | 13841815 | No | How Powerful are Graph Neural Networks? | 2018-10-01 | Code |
| 8 | GCN+virtual node | 12484310 | No | Semi-Supervised Classification with Graph Convol... | 2016-09-09 | Code |
| 9 | GPS | 12454066 | No | Recipe for a General, Powerful, Scalable Graph T... | 2022-05-25 | Code |
| 10 | GIN | 12390715 | No | How Powerful are Graph Neural Networks? | 2018-10-01 | Code |
| 11 | EGC-S (No Edge Features) | 11156530 | No | Do We Need Anisotropic Graph Neural Networks? | 2021-04-03 | Code |
| 12 | GCN | 11033210 | No | Semi-Supervised Classification with Graph Convol... | 2016-09-09 | Code |
| 13 | GAT | 11030210 | No | Graph Attention Networks | 2017-10-30 | Code |
| 14 | PNA (No Edge Features) | 10992050 | No | Do We Need Anisotropic Graph Neural Networks? | 2021-04-03 | Code |
| 15 | EGC-M (No Edge Features) | 10986002 | No | Do We Need Anisotropic Graph Neural Networks? | 2021-04-03 | Code |
| 16 | MPNN-Max (No Edge Features) | 10971506 | No | Do We Need Anisotropic Graph Neural Networks? | 2021-04-03 | Code |
| 17 | DiffPool w/ graphSAGE | 10095826 | No | Hierarchical Graph Representation Learning with ... | 2018-06-22 | Code |
| 18 | GraphTrans (GCN-Virtual) | 9053246 | No | - | - | - |
| 19 | GraphTrans (GCN) | 7563746 | No | - | - | - |