xrosliang / gaug

Implementation for AAAI'21 paper: Data Augmentation for Graph Neural Networks

Data Augmentation for Graph Neural Networks

This is the implementation of the proposed GAugM and GAugO and baselines.

Requirements

• Python 3.7.6
• Please refer to requirements.txt for all the packages used.

Usage

The scripts for hyperparameter search with Optuna are optuna_[method].py.

All the parameters are included in best_parameters.json. Results can be reproduced with the scripts train_[method].py, which will automatically load the parameters. For example, to reproduce the result of GAugO with GCN on Cora, you can simply run:

python train_GAugO.py --dataset cora --gnn gcn --gpu 0

Data

The format of data files are described in detail in the file data/README. Due to file size limit, for GAugM, only the edge_probabilities of Cora is provided. Please find the all edge_probabilities files at https://tinyurl.com/gaug-data. The VGAE implementation I used for generating these edge_probabilities are also provided under the folder vgae/.

Cite

If you find this repository useful in your research, please cite our paper:

@inproceedings{zhao2021data,
  title={Data Augmentation for Graph Neural Networks},
  author={Zhao, Tong and Liu, Yozen and Neves, Leonardo and Woodford, Oliver and Jiang, Meng and Shah, Neil},
  booktitle={The Thirty-Fifth AAAI Conference on Artificial Intelligence},
  pages={},
  year={2021}
}