Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
Abstract
Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases in node attributes and graph structure, leading to unfair predictions. To address these challenges, we propose a novel GNN framework, DAB-GNN, that Disentangles, Amplifies, and deBiases attribute, structure, and potential biases in the GNN mechanism. DAB-GNN employs a disentanglement and amplification module that isolates and amplifies each type of bias through specialized disentanglers, followed by a debiasing module that minimizes the distance between subgroup distributions to ensure fairness. Extensive experiments on five datasets demonstrate that DAB-GNN significantly outperforms ten state-of-the-art competitors in terms of achieving an optimal balance between accuracy and fairness.
Cite
Text
Lee et al. "Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I11.33308Markdown
[Lee et al. "Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/lee2025aaai-disentangling/) doi:10.1609/AAAI.V39I11.33308BibTeX
@inproceedings{lee2025aaai-disentangling,
title = {{Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks}},
author = {Lee, Yeon-Chang and Shin, Hojung and Kim, Sang-Wook},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {12013-12021},
doi = {10.1609/AAAI.V39I11.33308},
url = {https://mlanthology.org/aaai/2025/lee2025aaai-disentangling/}
}