As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important. However, due to the node dependency in the graph-structured data, representation unlearning in Graph Neural Networks (GNNs) is challenging and less well explored. In this paper, we fill in this gap by first studying the unlearning problem in linear-GNNs, and then introducing its extension to non-linear structures. Given a set of nodes to unlearn, we propose PROJECTOR that unlearns by projecting the weight parameters of the pre-trained model onto a subspace that is irrelevant to features of the nodes to be forgotten. PROJECTOR could overcome the challenges caused by node dependency and enjoys a perfect data removal, i.e., the unlearned model parameters do not contain any information about the unlearned node features which is guaranteed by algorithmic construction. Empirical results on real-world datasets illustrate the effectiveness and efficiency of PROJECTOR.

翻译：随着隐私保护日益受到关注，从预训练图学习模型中消除特定节点的影响变得同等重要。然而，由于图结构数据中节点间的依赖性，图神经网络（GNN）中的表征遗忘具有挑战性且尚未得到充分探索。本文首先研究线性图神经网络的遗忘问题，进而将其扩展至非线性结构，以此填补这一空白。针对需要遗忘的节点集，我们提出PROJECTOR方法，该方法通过将预训练模型的权重参数投影到与待遗忘节点特征无关的子空间来实现遗忘。PROJECTOR能够克服节点依赖性带来的挑战，并实现完美的数据移除——即通过算法构造保证，遗忘后的模型参数不包含任何被遗忘节点特征的信息。在实际数据集上的实验结果表明了PROJECTOR的有效性与高效性。