Towards More Practical Adversarial Attacks on Graph Neural Networks

This repo provides the official implementations for the experiments described in the following paper:

Towards More Practical Adversarial Attacks on Graph Neural Networks

Jiaqi Ma*, Shuangrui Ding*, and Qiaozhu Mei. NeurIPS 2020.

(*: equal constribution)

A previous version of this paper is appeared with the title Black-Box Adversarial Attacks on Graph Neural Networks with Limited Node Access.

Update: The reader is encouraged to look at this repo, which is a follow-up work published in WSDM 2022. This follow-up work has an improved experiment setup as well as a full implementation of RWCS and GC-RWCS.

Requirements

• dgl 0.4.2
• torch 1.4.0
• networkx 2.3
• numpy 1.16.4

Run the code

Example command to run the code: python main.py --dataset cora --model JKNetMaxpool --threshold 0.1 --steps 4.

Cite

@inproceedings{ma2020practical,
  title={Towards More Practical Adversarial Attacks on Graph Neural Networks},
  author={Ma, Jiaqi and Ding, Shuangrui and Mei, Qiaozhu},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}