A PyTorch implementation for the paper below:
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
To run the code, you need to have at least Python 3.7 or later versions.
1.In CSGNN/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 --config ./config/csgnn_sichuan.yml to run CSGNN with default settings on Sihcuan dataset;
-Run python main.py --config ./config/csgnn_bupt.yml to run CSGNN with default settings on BUPT 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 CSGNN;model.py: CSGNN model implementations;utils.py: utility functions.
@article{hu2023cost,
title={Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection},
author={Hu, Xinxin and Chen, Haotian and Chen, Hongchang and Liu, Shuxin and Li, Xing and Zhang, Shibo and Wang, Yahui and Xue, Xiangyang},
journal={IEEE Transactions on Computational Social Systems },
year={2023},
doi={10.1109/TCSS.2023.3302651}
}