Dataset: PPI
Library: PyG
This repository contains code to benchmark knowledge distillation for GNNs on the PPI dataset, developed in the PyG framework. The main purpose of the codebase is to:
- Train teacher GAT models on PPI dataset via supervised learning and export the checkpoints
- Train student GAT models with/without knowledge distillation.
.
├── checkpoints
├── logs
├── data # automatically created by OGB data downloaders
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├── scripts # scripts to conduct full experiments and reproduce results
│ ├── baselines.sh # script to train student models without KD
│ ├── run.sh # script to benchmark all KD losses
│ └── train_teacher.sh # script to train and save teacher checkpoints
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├── README.md
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├── criterion.py # KD loss functions
├── gnn.py # train student GNNs via auxiliary representation distillation loss
├── train_teacher.py # train teacher GNNs and export checkpoints
├── logger.py # logging utilities
└── submit.py # read log directory to aggregate results
For full usage, each file has accompanying flags and documentation.
Also see the scripts folder for reproducing results.