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.

随着对数据隐私的关注加剧，图神经网络中的遗忘概念成为学术界一个重要的研究前沿，在用户请求下，遗忘概念可以强制从经过训练的图神经网络中有选择地删除特定数据。我们的研究专注于边的遗忘，这一过程在现实世界应用中具有广泛适用性。然而，我们的研究发现当前最先进的方法存在一个关键限制，被称为过度遗忘，即遗忘过程在去除特定数据之外错误地移除了过多信息，从而显著降低了剩余边的预测准确性。为了解决这个问题，我们确定了 GNNDelete 的损失函数作为过度遗忘现象的主要原因，并且我们的分析也表明，损失函数对于有效的边的遗忘可能不是必要的。基于这些认识，我们简化了 GNNDelete 方法，开发出一种名为 UtU 的新方法，它通过对图结构中的忘记边进行断开来方便遗忘。我们进行了大量实验证明，与重新训练模型相比，UtU 在提供隐私保护的同时保持了高准确性。具体而言，UtU 保留了超过 97.3% 的重新训练模型的隐私保护能力和 99.8% 的链接预测准确性。同时，UtU 仅需要恒定的计算需求，凸显了其作为一种轻量且实用的边的遗忘解决方案的优势。