TCGU: Data-centric Graph Unlearning based on Transferable Condensation

TL;DR

This paper introduces TCGU, a data-centric graph unlearning method that efficiently revokes data influence without accessing deleted data, preserving model utility and privacy in zero-glance settings.

Contribution

The paper proposes a novel zero-glance graph unlearning approach using transferable condensation and distribution matching, addressing efficiency and privacy issues of prior methods.

Findings

Outperforms existing GU methods in utility, efficiency, and efficacy.

Effective in zero-glance privacy settings with immediate data deletion.

Validated on 6 benchmark datasets.

Abstract

With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attention. However, existing exact unlearning methods suffer from either low efficiency or poor model performance. While being more utility-preserving and efficient, current approximate unlearning methods are not applicable in the zero-glance privacy setting, where the deleted samples cannot be accessed during unlearning due to immediate deletion requested by regulations. Besides, these approximate methods, which try to directly perturb model parameters still involve high privacy concerns in practice. To fill the gap, we propose Transferable Condensation Graph Unlearning (TCGU), a data-centric solution to zero-glance graph unlearning. Specifically, we first design a two-level alignment strategy to pre-condense the original graph into a small yet utility-preserving dataset. Upon receiving an unlearning request, we fine-tune the pre-condensed data with a low-rank plugin, to directly align its distribution with the remaining graph, thus efficiently revoking the information of deleted data without accessing them. A novel similarity distribution matching approach and a discrimination regularizer are proposed to effectively transfer condensed data and preserve its utility in GNN training, respectively. Finally, we retrain the GNN on the transferred condensed data. Extensive experiments on 6 benchmark datasets demonstrate that TCGU can achieve superior performance in terms of model utility, unlearning efficiency, and unlearning efficacy than existing GU methods.