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arXiv · 2403.07353

Graph Unlearning with Efficient Partial Retraining

Jiahao Zhang·Lin Wang·Shijie Wang·Wenqi Fan
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Abstract

Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training graph into subgraphs and trains sub-models on them, allowing fast unlearning through partial retraining. However, the graph partition process causes information loss in the training graph, resulting in the low model utility of sub-GNN models. In this paper, we propose GraphRevoker, a novel graph unlearning framework that better maintains the model utility of unlearnable GNNs. Specifically, we preserve the graph property with graph property-aware sharding and effectively aggregate the sub-GNN models for prediction with graph contrastive sub-model aggregation. We conduct extensive experiments to demonstrate the superiority of our proposed approach.

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Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan. 2024-03-12. Graph Unlearning with Efficient Partial Retraining. https://arxiv.org/abs/2403.07353

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