Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships

The Offical Code of Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships

Requirements

The code has been implemented and tested with Python 3.10.0. To install the required packages:

$ pip install -r requirements.txt

Usage

Training Commands

### Evaluation Commands
python main.py --dataset DATASET     Name of dataset
  --model {GCN,GIN,GCNRU,GINRU}
                        Name of model
  --seed SEED           Random seed (default: 2023)
  --n_splits N_SPLITS   Number of splits
  --n_repeats N_REPEATS
                        Number of times cross-validation needs to be repeated
  --batch_size BATCH_SIZE
                        Input batch size for training (default: 128)
  --lr LR               Learning rate (default: 0.001)
  --weight_decay WEIGHT_DECAY
                        Weight decay (L2 penalty) (default: 5e-4)
  --gradient_clip_val GRADIENT_CLIP_VAL
                        The value at which to clip gradients
  --patience PATIENCE   Number of validation epochs with no improvement after which training will be stopped
  --min_epochs MIN_EPOCHS
                        Force training for at least `min_epochs` epochs
  --max_epochs MAX_EPOCHS
                        Stop training once `max_epochs` is reached
  --hidden_dim HIDDEN_DIM
                        Number of hidden units (default: 128)
  --num_layers NUM_LAYERS
                        Number of layers (default: 5)
  --alpha ALPHA         The parameter controls the balance between the Cross Entropy loss and the distillation loss
  --beta BETA           Weight of representation hints loss
  --temp TEMP           Temperature to smooth the logits
  --cuda