Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that existing unlearning strategies tend to apply uniform parameter selection and editing across all graph neural network (GNN) layers, which is especially harmful for multimodal graphs where high-dimensional input projections encode dominant cross-modal knowledge. As a result, over-editing these sensitive layers often leads to catastrophic utility degradation after forgetting, undermining both stable learning and effective privacy protection. To address this gap, we propose FDQ, a Feature-Dimension Aware Quantile framework for multimodal graph unlearning. FDQ adaptively identifies high-dimensional input projection layers and applies more conservative, FDQ-guided quantile thresholds when constructing suppression sets, while keeping the underlying importance estimation mechanism unchanged. FDQ is seamlessly integrated with diagonal sensitivity-based parameter importance analysis to enable efficient node and edge unlearning under general forget requests. Through extensive experiments on Ele-Fashion and Goodreads-NC, we demonstrate that FDQ consistently achieves strong utility preservation while maintaining effective forgetting against membership inference attacks. Overall, FDQ offers a principled and robust solution for privacy-aware unlearning in high-dimensional multimodal graph systems.

翻译：图遗忘仍然是支持隐私保护与可持续多模态图学习的关键技术。然而，我们观察到现有遗忘策略倾向于在图神经网络（GNN）所有层中采用统一的参数选择与编辑，这对多模态图尤为有害，因为高维输入投影编码了主导性的跨模态知识。结果，对这些敏感层的过度编辑常导致遗忘后灾难性的效用退化，削弱了稳定学习与有效隐私保护。为填补这一空白，我们提出FDQ——一种面向多模态图遗忘的基于特征维度感知的分位数框架。FDQ自适应识别高维输入投影层，并在构建抑制集时应用更保守的、由FDQ引导的分位数阈值，同时保持底层重要性估计机制不变。FDQ与基于对角敏感性的参数重要性分析无缝集成，从而在一般遗忘请求下实现高效的节点与边遗忘。通过在Ele-Fashion与Goodreads-NC上的大量实验，我们证明FDQ在保持有效遗忘以抵御成员推断攻击的同时，始终能实现强大的效用保持。总体而言，FDQ为高维多模态图系统中隐私感知的遗忘提供了一种原则性且鲁棒的解决方案。