GFDN

[KDD 2023] Group-based Fraud Detection Network on e-Commerce Platforms

https://doi.org/10.1145/3580305.3599836

QueryOPT

The project that helps us implement (alpha, beta)-core is QueryOPT.

Quick Start

0. Prepare the environment:

   conda create -n gfdn python=3.8
   conda activate gfdn
   conda install pytorch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1 cudatoolkit=10.1 -c pytorch
   pip install torch-scatter==2.0.8 -f https://pytorch-geometric.com/whl/torch-1.8.1+cu101.html && pip install torch-sparse==0.6.12 -f https://pytorch-geometric.com/whl/torch-1.8.1+cu101.html && pip install torch-geometric==2.0.0
   

1. download the dataset from the competition. Please download the final round dataset.
2. Pre-process: python dataset/get_data.py
3. Install swig
4. Build pyabcore: sudo apt-get -y install libboost-all-dev && cd ./queryopt && ./build.sh && cd ..
5. Start:python main.py

Note

The last four entries of customer vertex in the dataset are noise and have been ignored. Experimental results may vary slightly due to different hardware configurations.