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

Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection