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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).

framework

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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A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

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