相关信息 通过超图形转换增强图形异常检测
IEEE transactions on cybernetics
|April 18, 2025
概括
本研究介绍了相关信息增强图形异常检测 (CIE-GAD),以解决图形数据中的异常伪装问题. 通过学习边缘共发生关系和融合多频信号,CIE-GAD提高了检测.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 网络分析 网络分析
背景情况:
- 图形异常检测 (GAD) 对于欺诈检测和网络安全等应用至关重要.
- 图形神经网络 (GNN) 对GAD来说很强大,但与异常伪装作斗争,其中异常类似于正常数据.
- 现有的GNN模型面临着由于特征相似性而区分微妙异常的挑战.
研究的目的:
- 提出一种新的方法,即相关信息增强图形异常检测 (CIE-GAD),以克服GAD中的异常伪装.
- 通过有效利用图边之间的相关信息来提高GNN检测异常的能力.
- 为了提高复杂的,现实世界的图形数据集上的GAD方法的性能和稳定性.
主要方法:
- 构建一个超图,以模拟相邻边缘之间的共发生关系,捕捉正常和异常样本之间的微妙差异.
- 开发一种带有节点级注意力融合的光谱卷积机制,以捕获多频信号并减轻局部异构性.
- 增强样本相关性信息的提取,以抵消由异常伪装引起的特征相似性.
主要成果:
- 在各种现实数据集上,CIE-GAD显著超过了最先进的GAD方法.
- 在AUC-PR (精度回忆曲线下的面积) 中实现了高达3.47%的改善,平均收益为1.5%.
- 在检测异常方面表现出有效性,即使它们与正常实例具有特征相似性.
结论:
- 拟议的CIE-GAD有效地解决了在图形异常检测中异常伪装的挑战.
- 新的超图结构和光谱卷积机制增强了GNN对强大的GAD的能力.
- 在各种应用中,CIE-GAD为检测各种应用中复杂的图形结构数据中的异常提供了有希望的进步.
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