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 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, owing to its widespread applicability. Current state-of-the-art approaches like GNNDelete can eliminate the influence of specific edges, yet our research has revealed a critical limitation in these approaches, termed over-forgetting. It occurs when the unlearning process inadvertently removes excessive information beyond specific data, leading to a significant decline in prediction accuracy for the remaining edges. To address this issue, we have identified the loss functions of GNNDelete as the primary source of the over-forgetting phenomenon. Furthermore, our analysis also suggests that loss functions may not be essential for effective edge unlearning. Building on these insights, we have simplified GNNDelete to develop 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. Specifically, UtU upholds 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：[摘要] 随着数据隐私问题的日益突出，图神经网络（GNN）中的遗忘学习已成为学术界的重要研究前沿。这一概念对于落实"被遗忘权"至关重要，即根据用户请求从已训练的GNN中有选择性地删除特定数据。本研究聚焦于边删除学习——因其广泛适用性而具有实际应用价值的重要过程。当前最先进的方法（如GNNDelete）虽能消除特定边的影响，但我们的研究揭示其存在关键缺陷——过度遗忘。这一现象表现为遗忘过程在移除特定数据时意外清除了过多信息，导致剩余边的预测准确率显著下降。针对该问题，我们确认GNNDelete的损失函数是造成过度遗忘的主要根源。进一步分析表明，损失函数对有效的边删除学习可能并非必要。基于这些发现，我们对GNNDelete进行简化，提出"解链即遗忘"（UtU）这一新方法：仅通过从图结构中解除遗忘边即可实现遗忘。大量实验证明，UtU在提供与重训练模型相当隐私保护能力的同时，能保持下游任务的高准确性。具体而言，UtU保持了重训练模型97.3%以上的隐私保护能力和99.8%的链路预测准确率。此外，UtU仅需恒定计算开销，凸显其作为极轻量化边删除学习解决方案的实用优势。