Graph Unlearning Meets Influence-aware Negative Preference Optimization

• URL: http://arxiv.org/abs/2510.19479v1
• Date: Wed, 22 Oct 2025 11:18:00 GMT
• Title: Graph Unlearning Meets Influence-aware Negative Preference Optimization
• Authors: Qiang Chen, Zhongze Wu, Ang He, Xi Lin, Shuo Jiang, Shan You, Chang Xu, Yi Chen, Xiu Su,
• Abstract summary: In this paper, we introduce textbfINPO, an textbfInfluence-aware textbfNegative textbfPreference textbfOptimization framework.<n>We first analyze that NPO has slower divergence speed and theoretically propose that unlearning high-influence edges can reduce impact of unlearning.<n>Experiments conducted on five real-world datasets demonstrate that INPO-based model achieves state-of-the-art performance on all forget quality metrics.
• Score: 45.33243345077153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in graph unlearning models have enhanced model utility by preserving the node representation essentially invariant, while using gradient ascent on the forget set to achieve unlearning. However, this approach causes a drastic degradation in model utility during the unlearning process due to the rapid divergence speed of gradient ascent. In this paper, we introduce \textbf{INPO}, an \textbf{I}nfluence-aware \textbf{N}egative \textbf{P}reference \textbf{O}ptimization framework that focuses on slowing the divergence speed and improving the robustness of the model utility to the unlearning process. Specifically, we first analyze that NPO has slower divergence speed and theoretically propose that unlearning high-influence edges can reduce impact of unlearning. We design an influence-aware message function to amplify the influence of unlearned edges and mitigate the tight topological coupling between the forget set and the retain set. The influence of each edge is quickly estimated by a removal-based method. Additionally, we propose a topological entropy loss from the perspective of topology to avoid excessive information loss in the local structure during unlearning. Extensive experiments conducted on five real-world datasets demonstrate that INPO-based model achieves state-of-the-art performance on all forget quality metrics while maintaining the model's utility. Codes are available at \href{https://github.com/sh-qiangchen/INPO}{https://github.com/sh-qiangchen/INPO}.

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