提出自适应图删去框架，精准删除节点边且不伤其余图结构。
Adaptive Graph Unlearning
- 根据任务需求自适应调整删去策略，适配多种图神经网络。
- 在7个真实数据集上实现更优的删去效果与效率。
- 适合需动态更新图数据的隐私或纠错场景。
图未学习（Graph unlearning）旨在从训练好的图神经网络（GNN）中删除节点、边等图元素，对包含过时、错误或敏感信息的实际应用至关重要。然而，现有方法常因忽视不同删去任务的差异目标，导致删去不完全或过度删去；同时难以准确识别各类GNN架构中受删去影响的邻居。为此，我们提出新型自适应图未学习框架AGU，可灵活适配多样删去任务与GNN架构。AGU确保被删元素被彻底遗忘，同时保持剩余图结构完整，精准识别各架构受影响邻居，并优先处理重要邻居以提升删去性能。在七个真实世界图上的大量实验表明，AGU在有效性、效率和删去能力方面均优于现有方法。
原文摘要 · Abstract (English)
Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contain outdated, inaccurate, or privacy-sensitive information. However, existing methods often suffer from (1) incomplete or over unlearning due to neglecting the distinct objectives of different unlearning tasks, and (2) inaccurate identification of neighbors affected by deleted elements across various GNN architectures. To address these limitations, we propose AGU, a novel Adaptive Graph Unlearning framework that flexibly adapts to diverse unlearning tasks and GNN architectures. AGU ensures the complete forgetting of deleted elements while preserving the integrity of the remaining graph. It also accurately identifies affected neighbors for each GNN architecture and prioritizes important ones to enhance unlearning performance. Extensive experiments on seven real-world graphs demonstrate that AGU outperforms existing methods in terms of effectiveness, efficiency, and unlearning capability.
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