SAMCL:用于检测图形异常的子图对齐多视图对比学习
IEEE transactions on neural networks and learning systems
|November 7, 2023
概括
这项研究引入了SAMCL,一种新的图形异常检测方法,它比较子图,而不仅仅是节点. 它通过对准子图相似性来有效地识别异常,优于现有技术.
科学领域:
- 图形理论是指图形的理论.
- 机器学习是机器学习.
- 数据挖掘是一种数据挖掘.
背景情况:
- 图形异常检测 (GAD) 对社交和金融系统等网络至关重要.
- 图形对比学习 (GCL) 是一个领先的GAD方法,但往往忽略了子图-子图比较.
- 由于大小或节点索引差异,现有的GCL方法与非对齐的子图对进行斗争.
研究的目的:
- 为图形异常检测提出一种新的分图对齐的多视图对比方法 (SAMCL).
- 通过引入子图-子图水平对比来解决现有的GCL方法的局限性.
- 为了克服"非对齐"问题,在子图对对比中进行准确的相似度测量.
主要方法:
- 通过捕捉目标节点的各种邻居来生成多视图增强子图.
- 开发了一个使用地球移动器距离 (EMD) 的子图对齐策略,用于非对齐的子图相似性,考虑节点嵌入和拓.
- 集成的子图对齐的对比学习,视图内节点-子图对比学习,以及用于异常得分的掩盖子图重建.
主要成果:
- SAMCL有效地解决了GAD中的子图-子图对比级别差距.
- 拟议的分图对齐策略准确地测量了非对齐的分图对之间的相似性.
- 实验表明,与最先进的方法相比,ACM数据集的性能有了显著的提高,在ACM数据集上提高了6.36%.
结论:
- SAMCL通过结合子图-子图对比,为图形异常检测提供了一种新且有效的方法.
- 该方法成功处理非对齐子图比较,提高异常检测的准确性.
- 结合的对比学习和重建模块提供了一个强大的框架,用于识别图形数据中的异常.
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