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}
}