As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of other samples. Recently, many frameworks for machine unlearning have been proposed, and most of them focus on image and text data. To extend machine unlearning to graph data, \textit{GraphEraser} has been proposed. However, a critical issue is that \textit{GraphEraser} is specifically designed for the transductive graph setting, where the graph is static and attributes and edges of test nodes are visible during training. It is unsuitable for the inductive setting, where the graph could be dynamic and the test graph information is invisible in advance. Such inductive capability is essential for production machine learning systems with evolving graphs like social media and transaction networks. To fill this gap, we propose the \underline{{\bf G}}\underline{{\bf U}}ided \underline{{\bf I}}n\underline{{\bf D}}uctiv\underline{{\bf E}} Graph Unlearning framework (GUIDE). GUIDE consists of three components: guided graph partitioning with fairness and balance, efficient subgraph repair, and similarity-based aggregation. Empirically, we evaluate our method on several inductive benchmarks and evolving transaction graphs. Generally speaking, GUIDE can be efficiently implemented on the inductive graph learning tasks for its low graph partition cost, no matter on computation or structure information. The code will be available here: https://github.com/Happy2Git/GUIDE.

翻译：作为在机器学习中实现“被遗忘权”的一种方式，机器遗忘旨在从已训练模型中完全移除待删除样本的贡献和信息，同时不影响其他样本的贡献。近年来，许多机器遗忘框架已被提出，其中大多数聚焦于图像和文本数据。为将机器遗忘扩展到图数据，研究者提出了GraphEraser。然而，一个关键问题在于GraphEraser专为直推式图设置设计，其中图是静态的，且测试节点的属性与边在训练阶段可见。它不适用于归纳式设置，其中图可以是动态的，且测试图信息无法预先知晓。这种归纳能力对于社交媒体和交易网络等图不断演化的生产环境机器学习系统至关重要。为填补这一空白，我们提出了GUIDE（Guided Inductive Graph Unlearning）框架。GUIDE包含三个组件：兼顾公平性与平衡性的引导式图划分、高效子图修复以及基于相似性的聚合。实验方面，我们在多个归纳式基准数据集和动态交易图上评估了该方法。总体而言，GUIDE凭借其较低的图划分成本（无论计算量还是结构信息），可高效地应用于归纳式图学习任务。代码地址：https://github.com/Happy2Git/GUIDE。