HGNN Shield:保护超图神经网络免受高阶结构攻击
IEEE transactions on pattern analysis and machine intelligence
|December 26, 2025
概括
HGNN Shield 增强了超图神经网络的功能,可以对抗结构性攻击. 它使用超边缘依赖估计和高阶盾模块来提高超图应用中的稳定性和数据完整性.
科学领域:
- 机器学习 机器学习
- 网络科学 网络科学
- 网络安全 网络安全
背景情况:
- 超图神经网络 (HGNN) 使用超边缘模型复杂的高阶相关性.
- 高GNN容易受到结构性攻击和非理性的连接,降低性能.
研究的目的:
- 引入HGNN Shield,这是一个防御框架,旨在增强HGNN对结构性攻击的强度.
- 提高基于超图的应用程序的可靠性和安全性.
主要方法:
- 在HGNN Shield中包含了超边缘依赖估计 (HDE) 和高顺序盾 (HOS) 模块.
- HDE适应了超图的连接度量,优先考虑顶点依赖.
- 通过Hyperpath Cut,Link和Refine子模块检测,断开和改进敌对连接.
主要成果:
- HGNN Shield显著提高了针对目标攻击的稳定性和数据完整性.
- 该框架比现有方法平均提高了9.33%的性能.
- 在六个超图数据集中表现出卓越的性能.
结论:
- HGNN Shield提供了一种可认证的防御机制,可以抵御高阶结构性攻击.
- 该框架提升了基于超图的应用程序的安全性和可靠性.
- 通过将基于图的测量扩展到超图,并保持超路径完整性,在理论上做出贡献.
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