Implementation of KDD'23 paper GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification.
This repository has been tested with the following packages:
- Python == 3.8.13
- PyTorch == 1.11.0
- PyTorch Geometric == 2.0.4
Please follow official instructions to install Pytorch and Pytorch Geometric.
--dataset: name of the dataset. Could be one of['Cora', 'CiteSeer', 'PubMed', 'Amazon-Photo', 'Amazon-Computers', 'Coauthor-CS'].--data_path: the path to the dataset. The dataset will be downloaded to this path automatically when the code is executed for the first time.--imb_ratio: imbalance ratio.--net: GNN backbone. Could be one of['GCN, GAT, SAGE'].--gdc: way to get the weighted graph. Could be one of['ppr', 'hk', 'none'].
Please refer to args.py for the full hyper-parameters.
Pass the above hyper-parameters to main.py. For example:
python main.py --dataset Cora --data_path dataset/ --imb_ratio 100 --net GCN --gdc ppr
MIT License
Feel free to email (liwzh63 [AT] mail2.sysu.edu.cn) for any questions about this work.
The code is implemented based on GraphENS and ReNode.
If you find this work is helpful to your research, please consider citing our paper:
@article{li2023graphsha,
title={GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification},
author={Wen-Zhi Li and Chang-Dong Wang and Hui Xiong and Jian-Huang Lai},
journal={arXiv preprint arXiv:2306.09612},
year={2023}
}