同性恋边缘增强图神经网络用于高类同性恋变异学习
IEEE transactions on pattern analysis and machine intelligence
|December 5, 2025
概括
图形神经网络 (GNN) 由于高类同类变异 (CHV) 而难以检测异常. 一个新的模型,HEAug,通过生成低CHV边缘来解决这个问题,显著提高基准数据集的性能.
科学领域:
- 机器学习 机器学习
- 图形理论 图形理论
- 数据挖掘 数据挖掘
背景情况:
- 图形神经网络 (GNN) 擅长学习图形数据特征.
- 香草GNN在图形异常检测任务中表现不佳.
- 高类同类变异 (CHV) 被确定为GNN在异常检测方面的表现不佳的原因.
研究的目的:
- 从理论上证明和解决高CHV对GNN异常检测性能的影响.
- 提出一种新的GNN模型,HEAug,旨在减轻CHV的影响.
- 为了提高GNN在图形异常检测中的分类准确性.
主要方法:
- 提出并理论证明了Class Homophily Variance (CHV) 特性作为GNN异常检测的关键因素.
- 开发了同类边缘增强图形神经网络 (HEAug) 模型.
- HEAug使用自我注意机制生成具有低CHV的新边缘,并修改损失函数以惩罚异性边缘.
主要成果:
- 在八个基准数据集中,HEAug实现了最先进的性能,包括异常检测,无边缘节点分类和对抗性攻击场景.
- 在各种场景中证明了CHV理论和HEAug模型的有效性,包括一种新的异性恋攻击.
- 展示了HEAug利用功能相关但未连接的节点的能力.
结论:
- 高CHV是GNN在异常检测中的关键限制.
- 拟议的HEAug模型有效地解决了CHV的挑战,从而实现了卓越的性能.
- 这些发现为改善图形异常检测和相关任务中的GNN提供了理论基础和实际解决方案.
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