GF_Attack

This repository is the official Tensorflow implementation of "A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models".

Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang, A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models, AAAI 2020.

Requirements

The script has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

• tensorflow (tested on 1.14.0)
• scipy (tested on 1.2.1)
• numpy (tested on 1.17.2)

Run

• 2 order of graph filter, selecting Top-128 smallest eigen-values/vectors.

python main.py --dataset cora --K 2 --T 128

We only did Top-128 and Top-Half largest eigen-values/vectors to get the results in paper. To get better performance, tuning the hyper-parameters is highly encouraged.

Acknowledgement

This repo is modified from NETTACK, and we sincerely thank them for their contributions.

Reference

• If you find GF-Attack useful in your research, please cite the following in your manuscript:

@inproceedings{chang2020restricted,
  title={A restricted black-box adversarial framework towards attacking graph embedding models},
  author={Chang, Heng and Rong, Yu and Xu, Tingyang and Huang, Wenbing and Zhang, Honglei and Cui, Peng and Zhu, Wenwu and Huang, Junzhou},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={3389--3396},
  year={2020}
}