PyTorch implementation for the paper below:
BTG: A Bridge to Graph machine learning in telecommunications fraud detection

To run the code, you need to have at least Python 3.7 or later versions. And follow the steps below :
1.Go to this site to download the 4 datasets, namely train_app.csv,train_sms.csv,train_user.csv,train_voc.csv;
2.Put the 4 downloaded datasets in the path: /BTG/data/train;
3.Run python data_process.py to extract features and generate dataset in DGL;
4.Run python train.py to run BTG with default settings on the dataset.

The repository is organized as follows:

• data_process.py: convert raw node features and adjacency matrix to DGL dataset;
• main.py: training and testing BTG;
• model.py: BTG model implementations;
• layers.py: model layers;
• utils.py: utility functions;
• data: raw dataset in /data/train, and extracted dataset in /data/user_data.

@article{hu2022btg,
  title={BTG: A Bridge to Graph machine learning in telecommunications fraud detection},
  author={Hu, Xinxin and Chen, Hongchang and Liu, Shuxin and Jiang, Haocong and Chu, Guanghan and Li, Ran},
  journal={Future Generation Computer Systems},
  volume={137},
  pages={274--287},
  year={2022},
  publisher={Elsevier}
}