发表机构
University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图遗忘在大规模删除下的灾难性遗忘问题，提出基于课程与偏好优化的CUNO框架，按难度渐进遗忘并优化，显著提升模型效用保持率。
AI 中文摘要
图遗忘旨在无需从头重新训练的情况下，从已训练的图模型中移除指定训练数据的影响。然而，现有方法在较大删除比例（大规模删除）下会出现模型效用的急剧下降，我们将这一现象称为灾难性遗忘。我们发现，一个关键原因是对所有被删除样本的均匀处理，这在图学习中尤其有害：结构依赖性导致不同节点在学习模型中扮演截然不同的角色，而现有方法却对整个遗忘集应用相同的遗忘操作。基于这一洞察，我们提出了CUNO，一个基于课程的图遗忘框架，它通过多个阶段按估计的遗忘难度对样本排序，逐步移除遗忘集。CUNO在每个课程阶段进一步采用分布级别的负偏好优化（NPO）目标，引导模型远离其在当前遗忘子集上的原始行为，同时保持保留集的性能。我们的理论分析表明，当遗忘集涵盖广泛的遗忘难度范围时，课程设计最为有益，这一条件在大规模删除下自然满足。综合实验证实，CUNO能持续缓解灾难性遗忘：在20%删除比例下，它保留了原始效用的74%，而现有方法仅为26%-53%；即使在50%删除比例下，它仍能保持超过一半的原始效用。我们的代码已公开于https://this URL。
英文摘要
Graph unlearning removes the influence of designated training data from a trained graph model without retraining from scratch. However, existing methods suffer a sharp drop in model utility under large deletion ratios (mass deletion), a phenomenon we refer to as catastrophic unlearning. We find that a key cause is the uniform treatment of all deleted samples, which is particularly damaging in graph learning: structural dependencies cause different nodes to play vastly different roles in the learned model, yet existing methods apply the same forgetting operation to the entire forget set. Based on this insight, we propose CUNO, a curriculum-based graph unlearning framework that removes the forget set progressively, ordering samples by their estimated unlearning difficulty across multiple stages. CUNO further employs a distribution-level negative preference optimization (NPO) objective at each curriculum stage that steers the model away from its original behavior on the current forget subset while preserving retained performance. Our theoretical analysis shows that the curriculum design is most beneficial when the forget set spans a wide range of unlearning difficulty, a condition naturally satisfied under mass deletion. Comprehensive experiments confirm that CUNO consistently mitigates catastrophic unlearning: at 20% deletion, it retains 74% of the original utility compared to 26-53% for existing methods, and maintains more than half the original utility even at 50% deletion. Our code is publicly available at https://anonymous.4open.science/r/cuno-D4FF.
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