Evaluation script for various methods on common benchmark datasets via 10-fold cross validation, where a training fold is randomly sampled to serve as a validation set. Hyperparameter selection is performed for the number of hidden units and the number of layers with respect to the validation set:
- GCN
- GraphSAGE
- GIN
- Graclus
- Top-K Pooling
- SAG Pooling
- DiffPool
- EdgePool
- GlobalAttention
- Set2Set
- SortPool
- ASAPool
Run (or modify) the whole test suite via
$ python main.py