Graph Unlearning with Efficient Partial Retraining
<|reference_start|>Graph Unlearning with Efficient Partial Retraining: 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.<|reference_end|>
@article{zhang2024graph,
    title={Graph Unlearning with Efficient Partial Retraining},
    author={Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan},
    journal={arXiv preprint arXiv:2403.07353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07353},
    primaryClass={cs.LG cs.CR}
}