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}
}