Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial intelligence. Graph unlearning aims to quickly eliminate the effects of specific data on graph neural networks (GNNs). However, most existing deterministic graph unlearning frameworks follow a balanced partition-submodel training-aggregation paradigm, resulting in a lack of structural information between subgraph neighborhoods and redundant unlearning parameter calculations. To address this issue, we propose a novel Graph Structure Mapping Unlearning paradigm (GSMU) and a novel method based on it named Community-centric Graph Eraser (CGE). CGE maps community subgraphs to nodes, thereby enabling the reconstruction of a node-level unlearning operation within a reduced mapped graph. CGE makes the exponential reduction of both the amount of training data and the number of unlearning parameters. Extensive experiments conducted on five real-world datasets and three widely used GNN backbones have verified the high performance and efficiency of our CGE method, highlighting its potential in the field of graph unlearning.

本研究解决了现有图去学习框架在去除特定数据对图神经网络影响时存在的结构信息缺失和冗余参数计算问题。提出了一种新颖的图结构映射去学习范式（GSMU）及其基于的社区中心图擦除器（CGE）方法，通过将社区子图映射到节点，实现了节点级去学习操作的重建，显著减少了训练数据和去学习参数的数量。实验表明，CGE在多个数据集上的高效性和性能出色，具有重大应用潜力。 

以社区为中心的图去学习