| folder | description |
|---|---|
| Data | Datasets. |
| AD-GCL | AD-GCL implementation code is provided. |
| Dataset | # Nodes | # Edges | # Attribute | # Anomalies |
|---|---|---|---|---|
| Cora | 2,708 | 5,429 | 1,433 | 5.5% |
| Citeseer | 3,327 | 4,732 | 3,703 | 4.5% |
| Pubmed | 19,717 | 44,338 | 500 | 3.0% |
| Bitcoinotc | 5,881 | 35,592 | 128 | 5.1% |
| BITotc | 4,863 | 28,473 | 128 | 6.2% |
| BITalpha | 3,219 | 19,364 | 128 | 9.3% |
# Cora
python ./AD-GCL/run.py --dataset 'cora' --lr 5e-3 --num_epoch 200 --threshold 7 --gpu_id 0
# Citeseer
python ./AD-GCL/run.py --dataset 'citeseer' --lr 3e-3 --num_epoch 200 --threshold 6 --gpu_id 0
# Pubmed
python ./AD-GCL/run.py --dataset 'pubmed' --lr 4e-3 --num_epoch 100 --threshold 8 --gpu_id 0
# Bitcoinotc
python ./AD-GCL/run.py --dataset 'bitcoinotc' --lr 4e-4 --num_epoch 100 --threshold 8 --gpu_id 0
# BITotc
python ./AD-GCL/run.py --dataset 'bitotc' --lr 5e-4 --num_epoch 100 --threshold 7 --gpu_id 0
# BITalpha
python ./AD-GCL/run.py --dataset 'bitalpha' --lr 5e-3 --num_epoch 100 --threshold 8 --gpu_id 0- Python 3.8.13
- PyTorch 1.12.1
- dgl 0.4.3.post1
- Scipy 1.9.1
- Tqdm 4.64.1
If you find this repository useful in your research, please consider citing the following paper:
@inproceedings{xu2025revisiting,
title={Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective},
author={Xu, Yiming and Peng, Zhen and Shi, Bin and Hua, Xu and Dong, Bo and Wang, Song and Chen, Chen},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={12},
pages={12972--12980},
year={2025}
}