要删除的蒸:在图形网络中不学习与知识蒸
IEEE transactions on neural networks and learning systems
|October 9, 2025
概括
图形取消学习使用知识蒸有效地从图形神经网络 (GNN) 中删除数据. 这种新的方法,D2DGN,删除特定的图形元素,同时保留基本信息,提高合规性和效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形取消学习可以从训练有素的图形神经网络 (GNN) 中删除信息,这对于数据隐私和模型适应性至关重要.
- 现有的方法与复杂的图形依赖性作斗争,并产生显著的开销.
- 由于隐私法规和动态数据环境,需要高效和有效的图形取消学习.
研究的目的:
- 引入一种新的,高效的,与模型无关的图形取消学习框架,称为D2DGN.
- 解决现有方法在处理局部图形依赖性和开销成本方面的局限性.
- 有效地删除特定的图形元素,同时保持保留元素的知识.
主要方法:
- 开发了D2DGN,这是一个用于图形取消学习的知识蒸框架.
- 实施了将图形知识分为保留和删除集的策略.
- 利用基于响应的软目标和基于特征的节点嵌入,最小化KL分歧.
主要成果:
- D2DGN在节点和边缘取消学习任务中表现出卓越的表现,超过现有方法高达43.1% (AUC).
- 实现了高效率,改进了目标元素的删除,并保留了保留数据的性能.
- 展示了零开销成本,使其成为一个高效的解决方案.
结论:
- D2DGN为GNN中的图形取消学习提供了有效和高效的解决方案.
- 知识蒸方法成功地平衡了信息的删除和保留.
- D2DGN为遵守数据保护法规和管理不断变化的图形数据提供了实际框架.
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