GLADC

This is the code of "Deep Graph Level Anomaly Detection with Contrastive Learning".

Data Preparation

Here, we give three example experiment datasets including BZR, COX2 and DHFR. Other datasets can be obtained according to the link in the paper.

Experiment Environment

The required packages are shown in the requirements.txt.

Train

 python main.py

If you find that this code is useful for your research, please cite our paper:

    @article{luo2022deep,
    title={Deep graph level anomaly detection with contrastive learning},
    author={Luo, Xuexiong and Wu, Jia and Yang, Jian and Xue, Shan and Peng, Hao and Zhou, Chuan and Chen, Hongyang and Li, Zhao and Sheng, Quan Z},
    journal={Scientific Reports},
    volume={12},
    number={1},
    pages={19867},
    year={2022},
    publisher={Nature Publishing Group UK London}
    }