A PyTorch implementation for the paper below:
Who Are the Evil Backstage Manipulators: Boosting Graph Attention Networks against Deep Fraudsters.
To run the code, you need to have at least Python 3.7 or later versions.
1.In BANADA/data directory，rununzip BUPT.zip and unzip Sichuan.zip to unzip the datasets;
2.Run python data_process.py to generate Sichuan and BUPT dataset in DGL;
3.Run python main.py to run BANADA with default settings.
For other dataset and parameter settings, please refer to the arg parser in train.py. Our model supports both CPU and GPU mode.
The repository is organized as follows:
baselines/:code for all the baselines used in our paper;data/: dataset files;data_process.py: convert raw node features and adjacency matrix to DGL dataset;main.py: training and testing BANADA;model.py: BANADA model implementations;utils.py: utility functions for EarlyStopping,MixedDropout and MixedLinear.
You can find the baselines in baselines directory. For example, you can run Player2Vec using:
python Player2Vec_main.py @article{hu2023banada,
title={Who Are the Evil Backstage Manipulators: Boosting Graph Attention Networks against Deep Fraudsters},
author={Hu, Xinxin and Chen, Hongchang and Liu, Shuxin and Jiang, Haocong and Wang, Kai and Wang, Yahui},
journal={Computer Networks},
volume={227},
pages={1-11},
year={2023},
publisher={Elsevier}
}