A PyTorch implementation of "Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection", IEEE Transactions on Knowledge and Data Engineering (TKDE).
- python==3.6.1
- dgl==0.4.1
- matplotlib==3.3.4
- networkx==2.5
- numpy==1.19.2
- pyparsing==2.4.7
- scikit-learn==0.24.1
- scipy==1.5.2
- sklearn==0.24.1
- torch==1.8.1
- tqdm==4.59.0
To install all dependencies:
pip install -r requirements.txt
To train and evaluate on BlogCatalog:
python run.py --device cuda:0 --expid 1 --dataset BlogCatalog --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.6
To train and evaluate on Flickr:
python run.py --device cuda:0 --expid 2 --dataset Flickr --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.6
To train and evaluate on Cora:
python run.py --device cuda:0 --expid 3 --dataset cora --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.6
To train and evaluate on CiteSeer:
python run.py --device cuda:0 --expid 4 --dataset citeseer --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.4
To train and evaluate on PubMed:
python run.py --device cuda:0 --expid 5 --dataset pubmed --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.4
To train and evaluate on ACM:
python run.py --device cuda:0 --expid 6 --dataset ACM --runs 5 --auc_test_rounds 256 --alpha 1.0 --beta 0.2
If you use our code in your research, please cite the following article:
@article{zheng2021generative,
title={Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection},
author={Zheng, Yu and Jin, Ming and Liu, Yixin and Chi, Lianhua and Phan, Khoa T and Chen, Yi-Ping Phoebe},
journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
year={2021}
}