FairGU: Fairness-aware Graph Unlearning in Social Networks

2026-01-14

AI Summary

This work addresses the critical issue that existing graph unlearning methods often inadvertently leak or amplify sensitive attributes when deleting nodes, thereby compromising algorithmic fairness. To mitigate this, we propose FairGU—the first framework that explicitly integrates fairness guarantees into the graph unlearning process. FairGU employs a fairness-aware module in conjunction with a structure-preserving strategy to jointly optimize the removal of node influence while effectively suppressing the exposure and amplification of sensitive attributes. Extensive experiments on multiple real-world graph datasets demonstrate that FairGU significantly outperforms current graph unlearning and fairness-enhancing baselines, achieving substantial improvements in fairness metrics without sacrificing model utility.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• graph unlearning
• algorithmic fairness
• sensitive attributes
• social networks
• privacy preservation

Innovation

• fairness-aware
• graph unlearning
• sensitive attributes
• social networks
• privacy-preserving