DGL implementation of GAT for ogbn-proteins. Using some of the techniques from Bag of Tricks for Node Classification with Graph Neural Networks (https://arxiv.org/abs/2103.13355).
Requires DGL 0.5 or later versions.
For the best score, run gat.py and you should directly see the result.
python3 gat.pyFor the score of GAT+labels, run gat.py with --use-labels enabled and you should directly see the result.
python3 gat.py --use-labelsHere are the results over 10 runs.
| Method | Validation ROC-AUC | Test ROC-AUC | #Parameters |
|---|---|---|---|
| GAT | 0.9276 ± 0.0007 | 0.8747 ± 0.0016 | 2,475,232 |
| GAT+labels | 0.9280 ± 0.0008 | 0.8765 ± 0.0008 | 2,484,192 |
MWE-GCN and MWE-DGCN are GCN models designed for graphs whose edges contain multi-dimensional edge weights that indicate the strengths of the relations represented by the edges.
- DGL 0.5.2
- PyTorch 1.4.0
- OGB 1.2.0
- Tensorboard 2.1.1
To use MWE-GCN:
python main_proteins_full_dgl.py --model MWE-GCNTo use MWE-DGCN:
python main_proteins_full_dgl.py --model MWE-DGCNAdditional optional arguments include 'rand_seed' (the random seed), 'cuda' (the cuda device number, if available), 'postfix' (a string appended to the saved-model file)