FairGU: 社交网络中具有公平性的图去学习
FairGU: Fairness-aware Graph Unlearning in Social Networks
AI总结：
FairGU通过整合公平性模块和数据保护策略，提升社交网络中图去学习的公平性和实用性。
摘要：
图去学习已经作为一种关键机制，支持可持续和隐私保护的社交网络，使模型能够移除已删除节点的影响，从而更好地保护用户信息。然而，我们发现现有的图去学习技术在保护敏感属性方面不够充分，常常导致算法公平性比传统图学习方法下降。为了解决这一差距，我们引入了FairGU，一种具有公平性的图去学习框架，旨在在去学习过程中保持实用性和公平性。FairGU集成了专门的公平性模块和有效的数据保护策略，确保当节点被移除时，敏感属性不会意外放大或结构上暴露。通过在多个现实世界数据集上的广泛实验，我们证明FairGU在准确性和公平性指标上都优于最先进的图去学习方法和公平性增强的图学习基线。我们的发现突显了当前去学习实践中被忽视的风险，并将FairGU确立为下一代可持续社交网络系统中的稳健和公平的解决方案。代码可在https://github.com/LuoRenqiang/FairGU上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
