保护图形神经网络用于无监督图形异常检测
概括
这项研究引入了G3AD,这是一种用于无监督图形异常检测的新框架. G3AD有效地保护图形神经网络 (GNN) 免受异常,大大提高了复杂图形数据的检测性能.
科学领域:
- 图形神经网络的神经网络
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 无监督图形异常检测识别了图形数据中的异常值,没有标签.
- 图形神经网络 (GNN) 通过聚合邻里信息来学习节点表示.
- 异常破坏了GNN对邻近一致性的假设,降低了性能.
研究的目的:
- 解决图形异常对无监督环境中GNN的不利影响.
- 提出一个新的框架,G3AD,用于强大的无监督图形异常检测.
- 改进节点表示学习以增强异常识别.
主要方法:
- G3AD使用具有相关性约束的辅助网络来稳定GNN.
- 一个自适应缓存 (AC) 模块阻止了GNN重建异常图形数据.
- 该框架旨在与各种GNN骨干兼容.
主要成果:
- 在合成和现实世界数据集上,G3AD显著优于20种最先进的方法.
- 拟议的方法在不同的GNN架构中展示了强大的泛化能力.
- 在无监督图形异常检测任务中,G3AD实现了卓越的性能.
结论:
- 通过减轻GNN漏洞,G3AD为无监督图形异常检测提供了有效的解决方案.
- 该框架在存在图形异常的情况下提高了GNN的稳定性和性能.
- G3AD代表了学习图形中异常检测有效表示的重大进步.
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