TADDY: Anomaly detection in dynamic graphs via transformer
This repo covers an reference implementation for the paper “Anomaly detection in dynamic graphs via transformer” (TADDY).
Some codes are borrowed from Graph-Bert and NetWalk.
Requirments
- Python==3.8
- PyTorch==1.7.1
- Transformers==3.5.1
- Scipy==1.5.2
- Numpy==1.19.2
- Networkx==2.5
- Scikit-learn==0.23.2
Usage
Step 0: Prepare Data
python 0_prepare_data.py --dataset uci
Step 1: Train Model
python 1_train.py --dataset uci --anomaly_per 0.1
Cite
If this code is helpful, please cite the original paper:
@ARTICLE{liu2021anomaly,
author={Liu, Yixin and Pan, Shirui and Wang, Yu Guang and Xiong, Fei and Wang, Liang and Chen, Qingfeng and Lee, Vincent CS},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={Anomaly Detection in Dynamic Graphs via Transformer},
year={2021},
doi={10.1109/TKDE.2021.3124061}}
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