/Dual-SVDAE

Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks

Primary LanguagePythonMIT LicenseMIT

Source code of paper "Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks".

Run Model Training and Evaluation

Dual-SVDAE:

python main.py --dataset cora --module SVDAE --nu1 0.2 --nu2 0.2 --beta 0.4 --lr 0.001 --n-hidden 32 --n-layers 2 --weight-decay 0.0005 --n-epochs 5000

OC-SVM(Raw):

python main.py --dataset cora --module OCSVM --mode A

OC-SVM (DW):

python main.py --dataset cora --module OCSVM --mode X

Deep-SVDD (Attr):

python main.py --dataset cora --module SVDD_Attr --nu 0.2 --lr 0.002 --n-hidden 32 --n-layers 2 --weight-decay 0.0005 --n-epochs 2000

Deep-SVDD (Stru):

python main.py --dataset cora --module SVDD_Stru --nu 0.2 --lr 0.002 --n-hidden 32 --n-layers 2 --weight-decay 0.0005 --n-epochs 2000

GAE:

python main.py --dataset cora --module GAE -lr 0.002 --n-hidden 32 --n-layers 2  --n-epochs 2000

Dominant:

python main.py --dataset cora --module Dominant -lr 0.002 --n-hidden 32 --n-layers 2 --n-epochs 2000

OC-GNN:

python main.py --dataset cora --module --nu 0.2 --lr 0.002 --n-hidden 32 --n-layers 2 ---n-epochs 2000

Requirements:

pytorch>=1.4
DGL>=0.4.2
sklearn>=0.20.1
numpy>=1.16
networkx>=2.1
Pyod>=0.7.6
tensorflow>=1.4.0,<=1.12.0
gensim==3.6.0
DGL>=0.4.2

Cite

If you make use of this code in your own work, please cite our paper.

@article{zhang2021deep,
  title={Deep dual support vector data description for anomaly detection on attributed networks},
  author={Zhang, Fengbin and Fan, Haoyi and Wang, Ruidong and Li, Zuoyong and Liang, Tiancai},
  journal={International Journal of Intelligent Systems},
  year={2021},
  doi={https://doi.org/10.1002/int.22683},
  publisher={Wiley Online Library}
}