fairGNN-WOD: Fair Graph Learning Without Demographics
This repository contains the models trained as part of a reproduction effort of the paper fairGNN-WOD: Fair Graph Learning Without Demographics by Wang, Liu, Pan, Liu, Saeed, Qiu & Zhang (2025).
For more details see the full repository: https://github.com/adambujna/fairgnn-wod-reproduction/
If you use this repository in your work, please cite the original papers:
Wang, Z., Liu, F., Pan, S., Liu, J., Saeed, F., Qiu, M., & Zhang, W. fairGNN-WOD: Fair Graph Learning Without Demographics. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 (pp. 556–564).
@inproceedings{ijcai2025p63,
title = {fairGNN-WOD: Fair Graph Learning Without Complete Demographics},
author = {Wang, Zichong and Liu, Fang and Pan, Shimei and Liu, Jun and Saeed, Fahad and Qiu, Meikang and Zhang, Wenbin},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {556--564},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/63},
url = {https://doi.org/10.24963/ijcai.2025/63},
}
Chai, J., Jang, T., & Wang, X. (2022). Fairness without demographics through knowledge distillation. Advances in Neural Information Processing Systems, 35, 19152-19164.
@article{chai2022fairness,
title={Fairness without demographics through knowledge distillation},
author={Chai, Junyi and Jang, Taeuk and Wang, Xiaoqian},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={19152--19164},
year={2022}
}
Kipf, T. N. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
@article{kipf2016semi,
title={Semi-supervised classification with graph convolutional networks},
author={Kipf, TN},
journal={arXiv preprint arXiv:1609.02907},
year={2016}
}