As concerns over data privacy intensify, unlearning in Graph Neural Networks (GNNs) has emerged as a prominent research frontier in academia. This concept is pivotal in enforcing the \textit{right to be forgotten}, which entails the selective removal of specific data from trained GNNs upon user request. Our research focuses on edge unlearning, a process of particular relevance to real-world applications. Current state-of-the-art approaches like GNNDelete can eliminate the influence of specific edges yet suffer from \textit{over-forgetting}, which means the unlearning process inadvertently removes excessive information beyond needed, leading to a significant performance decline for remaining edges. Our analysis identifies the loss functions of GNNDelete as the primary source of over-forgetting and also suggests that loss functions may be redundant for effective edge unlearning. Building on these insights, we simplify GNNDelete to develop \textbf{Unlink to Unlearn} (UtU), a novel method that facilitates unlearning exclusively through unlinking the forget edges from graph structure. Our extensive experiments demonstrate that UtU delivers privacy protection on par with that of a retrained model while preserving high accuracy in downstream tasks, by upholding over 97.3\% of the retrained model's privacy protection capabilities and 99.8\% of its link prediction accuracy. Meanwhile, UtU requires only constant computational demands, underscoring its advantage as a highly lightweight and practical edge unlearning solution.

翻译：随着数据隐私问题的日益突出，图神经网络中的遗忘学习已成为学术界的重要研究前沿。这一概念对实现“被遗忘权”至关重要，即根据用户请求从已训练模型中选择性移除特定数据。本研究聚焦于边遗忘学习——这一与真实应用场景尤为相关的过程。当前如GNNDelete等最先进方法虽能消除特定边的影响，却存在“过度遗忘”问题：即遗忘过程会无意移除过量信息，导致剩余边的性能显著下降。我们的分析指出，GNNDelete的损失函数是过度遗忘的主要根源，同时揭示损失函数对有效边遗忘可能冗余。基于此，我们简化GNNDelete并开发出**解耦即遗忘**（Unlink to Unlearn, UtU）方法——仅通过从图结构中解耦待遗忘边即可实现遗忘。大量实验表明，UtU能提供与重训练模型同等水平的隐私保护，同时保持下游任务高精度：其隐私保护能力可达重训练模型的97.3%以上，链接预测精度达99.8%。此外，UtU仅需恒定计算开销，凸显其作为超轻量级、高实用性边遗忘解决方案的优势。