Towards effective edge unlearning: enhancing graph unlearning via contrastive learning with adversarial example

Graph unlearning involves removing certain elements, such as edges and nodes, from a trained graph neural network. This research has significant practical implications, such as enabling users to enforce data removal requests in compliance with their”right to be forgotten”. Additionally, it can help models regain utility by eliminating the impact of poisoned data. Although some graph unlearning algorithms, such as GNNDelete, assign forgotten samples to random negative samples to negate their impact on the model, using random samples is not the optimal choice as it may lead to decreased performance of the model on downstream tasks. Moreover, GNNDelete requires additional space to mark the forgotten samples and their neighborhoods, making this method inconvenient to implement and difficult to continue training. In light of this, we propose a novel graph unlearning framework called Adversarial Example-Based Graph Contrastive Unlearning (AEGCU). AEGCU generates adversarial examples through a straightforward method and utilizes contrastive learning to compare the features of the forgotten samples with those of the adversarial examples, thereby aiming to thoroughly erase edge information. Our experiments demonstrate that AEGCU effectively forgets edges in the graph while maximizing the retention of predictive capabilities. Our model improves performance on both the link prediction as well as the node classification tasks, especially on the node classification task, where we improve performance by approximately 11% over GNNDelete with taking 30% less time on average compared to the GNNDelete method.