FairGU: Fairness-aware Graph Unlearning in Social Networks

TL;DR

FairGU is a novel framework that enhances fairness preservation during graph unlearning in social networks, balancing utility and sensitive attribute protection to ensure equitable and privacy-preserving model updates.

Contribution

We propose FairGU, the first fairness-aware graph unlearning method that maintains both utility and fairness, addressing a key gap in existing unlearning techniques.

Findings

Outperforms state-of-the-art unlearning methods in accuracy and fairness.

Effectively protects sensitive attributes during node removal.

Demonstrates robustness across multiple real-world datasets.

Abstract

Graph unlearning has emerged as a critical mechanism for supporting sustainable and privacy-preserving social networks, enabling models to remove the influence of deleted nodes and thereby better safeguard user information. However, we observe that existing graph unlearning techniques insufficiently protect sensitive attributes, often leading to degraded algorithmic fairness compared with traditional graph learning methods. To address this gap, we introduce FairGU, a fairness-aware graph unlearning framework designed to preserve both utility and fairness during the unlearning process. FairGU integrates a dedicated fairness-aware module with effective data protection strategies, ensuring that sensitive attributes are neither inadvertently amplified nor structurally exposed when nodes are removed. Through extensive experiments on multiple real-world datasets, we demonstrate that FairGU consistently outperforms state-of-the-art graph unlearning methods and fairness-enhanced graph learning baselines in terms of both accuracy and fairness metrics. Our findings highlight a previously overlooked risk in current unlearning practices and establish FairGU as a robust and equitable solution for the next generation of socially sustainable networked systems. The codes are available at https://github.com/LuoRenqiang/FairGU.