# FairGU: Fairness-aware Graph Unlearning in Social Networks

arXiv: https://arxiv.org/abs/2601.09469 · HTML: https://mlsanity.com/paper/2601.09469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Graph unlearning, one of 12 directions in LLM safety and security.
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Placed in

- LLM safety and security / [Graph unlearning](https://mlsanity.com/safety/graph-unlearning/)

## Builds on (most cited first, 20 of 20)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage](https://mlsanity.com/paper/2206.03426/) (2022)
- [Towards Effective and General Graph Unlearning via Mutual Evolution](https://mlsanity.com/paper/2401.11760/) (2024)
- [FairGT: A Fairness-aware Graph Transformer](https://mlsanity.com/paper/2404.17169/) (2024)
- [Fair Attribute Completion on Graph with Missing Attributes](https://mlsanity.com/paper/2302.12977/) (2023)
- [Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning](https://mlsanity.com/paper/2409.16684/) (2024)
- [Endowing Pre-trained Graph Models with Provable Fairness](https://mlsanity.com/paper/2402.12161/) (2024)
- [Assessing Classifier Fairness with Collider Bias](https://mlsanity.com/paper/2010.03933/) (2020)
- [OpenGU: A Comprehensive Benchmark for Graph Unlearning](https://mlsanity.com/paper/2501.02728/) (2025)
- [TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds](https://mlsanity.com/paper/2403.12410/) (2024)
- [Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks](https://mlsanity.com/paper/2407.19183/) (2024)
- [Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework](https://mlsanity.com/paper/2508.15193/) (2025)
- [Graph Unlearning Meets Influence-aware Negative Preference Optimization](https://mlsanity.com/paper/2510.19479/) (2025)
- [Synthetic Forgetting without Access: A Few-shot Zero-glance Framework for Machine Unlearning](https://mlsanity.com/paper/2511.13116/) (2025)
- [CUFG: Curriculum Unlearning Guided by the Forgetting Gradient](https://mlsanity.com/paper/2509.14633/) (2025)

## Abstract (arXiv)

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.