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English(EN) Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection

新的JPGFN方法通过自适应滤波增强图异常检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的图异常检测方法JPGFN，该方法解决了现有频域滤波方法的局限性。JPGFN包含一个特征分离变换网络（FSTNN）以更好地学习细粒度节点特征，以及一个自适应雅可比多项式图滤波模块以捕捉复杂的频域特征。此外，它还包含一个节点标签约束模块，通过利用节点标签来提高性能。实验表明，JPGFN在各种真实数据集上的表现显著优于现有方法。 AI

影响 这项研究引入了一种新颖的图异常检测方法，有望提高识别复杂数据集中异常模式的准确性和效率。

排序理由 该集群包含一篇详细介绍图异常检测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的JPGFN方法通过自适应滤波增强图异常检测

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该集群包含一篇详细介绍图异常检测新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiang Wang, Zhijun Cheng, Zhenyu Meng ·

    特征变换增强的Jacobi多项式图滤波用于图异常检测

    arXiv:2608.27144v1 Announce Type: new Abstract: In recent years, graph anomaly detection (GAD) based on frequency-domain filtering have achieved promising results. However, existing approaches still face three major challenges: First, they use static basic function to constructed…

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