FairSAD

A PyTorch implementation of "Fair Graph Representation Learning via Sensitive Attribute Disentanglement"

Overview

FairSAD is a disentangled-based method to improve the fairness of GNNs while preserving task-related information. The core idea behind FairSAD is to minimize the impact of the sensitive attribute on final predictions.

Requirements

• numpy==1.21.6
• torch==1.13.1
• torch-cluster==1.5.9
• torch_geometric==2.3.1
• torch-scatter==2.1.1
• torch-sparse==0.6.17
• CUDA 11.7

Datasets download

To download datasets, please refer to these two repositories (NIFTY and FairGNN).

Reproduction

To reproduce our results, please run:

bash run.sh

Citation

If you find it useful, please cite our paper. Thank you!

@inproceedings{10.1145/3589334.3645532,
                author = {Zhu, Yuchang and Li, Jintang and Zheng, Zibin and Chen, Liang},
                title = {Fair Graph Representation Learning via Sensitive Attribute Disentanglement},
                publisher = {Association for Computing Machinery},
                address = {New York, NY, USA},
                url = {https://doi.org/10.1145/3589334.3645532},
                doi = {10.1145/3589334.3645532},
                booktitle = {Proceedings of the ACM on Web Conference 2024},
                pages = {1182–1192}
}