Implementations for NIFA

This repository includes the implementations for our paper at NeurIPS 2024: Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections.

Environments

Python 3.8.12

Packages:

dgl==0.6.1
dgl_cu110==0.6.1
numpy==1.21.4
torch==1.7.1+cu110
tqdm==4.62.3

Run the following code to install all required packages.

> pip install -r requirements.txt

Datasets & Processed files

• Due to size limitation, the processed datasets are stored in google drive as data.zip. The datasets include Pokec-z, Pokec-n and DBLP.

• Download and unzip the data.zip, and the full repository should be as follows:

  .
  ├── code
  │   ├── attack.py
  │   ├── main.py
  │   ├── model.py
  │   ├── run.sh
  │   └── utils.py
  ├── data
  │   ├── dblp.bin
  │   ├── pokec_n.bin
  │   └── pokec_z.bin
  ├── readme.md
  └── requirements.txt
  

Run the codes

All arguments are properly set below for reproducing our results.

python main.py --dataset pokec_z --alpha 0.01 --beta 4 --node 102 --edge 50 --before --device 0 --models 'GCN' 'GraphSAGE' 'APPNP' 'SGC'

python main.py --dataset pokec_n --alpha 0.01 --beta 4 --node 87 --edge 50 --before --device 1 --models 'GCN' 'GraphSAGE' 'APPNP' 'SGC'

python main.py --dataset dblp --alpha 0.1 --beta 8 --node 32 --edge 24 --epochs 500 --before --device 2 --models 'GCN' 'GraphSAGE' 'APPNP' 'SGC'

Licenses

This project is licensed under CC BY-NC-ND 4.0. To view a copy of this license, please visit http://creativecommons.org/licenses/by-nc-nd/4.0/

BibTeX

If you like our work and use the model for your research, please cite our work as follows:

@inproceedings{luo2024nifa,
author = {Luo, Zihan and Huang, Hong and Zhou, Yongkang and Zhang, Jiping and Chen, Nuo and Jin, Hai},
title = {Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections},
booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
year = {2024},
month = {October}
}