Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive. However, existing methods for graph unlearning either deteriorate model weights shared across all nodes or fail to effectively delete edges due to their strong dependence on local graph neighborhoods. To address these limitations, we introduce GNNDelete, a novel model-agnostic layer-wise operator that optimizes two critical properties, namely, Deleted Edge Consistency and Neighborhood Influence, for graph unlearning. Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion. GNNDelete updates representations to delete nodes and edges from the model while retaining the rest of the learned knowledge. We conduct experiments on seven real-world graphs, showing that GNNDelete outperforms existing approaches by up to 38.8% (AUC) on edge, node, and node feature deletion tasks, and 32.2% on distinguishing deleted edges from non-deleted ones. Additionally, GNNDelete is efficient, taking 12.3x less time and 9.3x less space than retraining GNN from scratch on WordNet18.
翻译:图形不学习, 包括删除节点、 节点标签等图形元素, 以及来自经过训练的图形神经网络( GNNN) 模型的关系, 这对于真实世界应用来说至关重要, 其中数据元素可能变得无关紧要、不准确或隐私敏感。 但是, 图表不学习的现有方法要么恶化了所有节点共享的模型权重, 要么由于对本地图形周围的强烈依赖而未能有效地删除边缘。 为了解决这些限制, 我们引入了GNNDelete, 这是一种新型的模型 -- -- 不可知层操作者, 优化了两个关键属性, 即, 即, 删除 End End End Encistity and Neborbority 影响, 其中数据元素可能变得无关紧要, 缩小了删除后的模型权重和相邻表达方式的影响。 GNNDemodel更新了演示文, 在7个真实世界图表上进行了实验, 显示GNNDeleeteleenne and Neblemental imal 上显示G- develop levelop 而不是删除了 GO 。</s>