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OR-Gate混合多尺度光谱图神经网络用于节点异常检测.

Zekang Li, Ruonan Liu, Dongyue Chen

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    |May 21, 2025
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    概括

    这项研究引入了一种新的多尺度光谱图神经网络 (MMGNN) 用于节点异常检测. MMGNN有效地挖掘高频图形信号,提高检测准确性和模型概括性.

    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:

    • 图形神经网络 (GNN) 广泛用于节点异常检测.
    • 现有的GNN经常充当低通波器,抑制关键的高频信号,导致过度平滑.
    • 这种限制阻碍了正常节点和异常节点之间的区别,并可能对数据增强产生负面影响.

    研究的目的:

    • 解决现有的GNN在节点异常检测方面的局限性.
    • 开发一种GNN架构,能够有效地挖掘高频图形信号.
    • 改进模型概括,降低图形异常检测中的计算成本.

    主要方法:

    • 提出了一种使用双平行结构的多尺度光谱GNN (MMGNN).
    • 通过叠加多项式光谱过器设计了多顺序的多尺度带宽过器.
    • 引入了用于光谱空间数据增强的or-gate混合.

    主要成果:

    • MMGNN有效地将高频信号挖掘到图形数据中.
    • 与传统的串行GNN结构相比,提出的方法可以降低计算成本.
    • 在四个现实数据集上的实验结果表明,MMGNN超越了最先进的方法.

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    结论:

    • 通过利用高频图形信号,MMGNN提供了一种有效的方法来检测节点异常.
    • 频谱过和数据增强策略提高了检测性能和模型概括性.
    • MMGNN为推进图形异常检测技术提供了一个有希望的方向.