对比的联合学习用于图形异常检测
IEEE transactions on neural networks and learning systems
|September 22, 2025
概括
本研究介绍了FedGAD,这是用于图形异常检测 (GAD) 的联合学习模型. FedGAD增强了节点表示,并有效地检测异常,即使在分散的客户端中数据不平衡.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 图形异常检测 (GAD) 面临着数据不平衡和隐私问题带来的挑战.
- 现有的GAD模型在优化节点嵌入和同时检测多种异常类型方面扎,导致精度降低.
研究的目的:
- 介绍FedGAD,这是一个用于图形异常检测 (GAD) 的新型联合学习模型.
- 解决现有的GAD方法中关于数据和隐私保护不平衡的局限性.
主要方法:
- FedGAD在分散的数据中心中采用协作无监督学习,而无需直接访问子图.
- 它通过掩盖和重建邻里特征来增强节点表示.
- 跨客户端节点表示模块可以使用来自其他客户端的信息进行邻居重建.
- 多尺度对比学习 (结构层面和上下文层面) 用于异常检测.
主要成果:
- 与基线方法相比,FedGAD在七个基准数据集上表现优越.
- 该模型有效地提高了GAD性能,特别是在客户之间数据分布不平衡的场景中.
结论:
- 在分散的环境中,FedGAD为图形异常检测提供了有效的解决方案.
- 该模型成功地克服了与数据和隐私不平衡相关的挑战,提高了GAD的准确性和稳定性.
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