特征变换增强雅可比多项式图滤波用于图异常检测

中文摘要 AI 辅助

近年来，基于频域滤波的图异常检测（GAD）已取得令人鼓舞的结果。然而，现有方法仍面临三大挑战：第一，它们使用静态基函数构建图滤波器，无法有效适配图数据的频域分布；第二，未能充分考虑节点特征向量中各属性的重要性信息，导致细粒度信息丢失；第三，未充分利用节点标签进行图异常检测。为解决这些问题，本文提出一种名为JPGFN（特征变换增强雅可比多项式图滤波网络）的新型图异常检测方法。首先，开发特征分离变换网络（FSTNN），通过特征分离及对不同维度的节点特征应用非线性变换，更好地学习细粒度节点特征。其次，基于雅可比多项式构建自适应雅可比多项式图滤波模块，以自适应捕捉图信号的复杂频域特征。最后，开发节点标签约束模块，促进节点标签的利用并提升图异常检测性能。在多个真实世界数据集上的实验结果表明，所提方法显著优于主流方法。

英文摘要

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 graph filter which cannot effectively adapt to the frequency-domain distribution of graph data. Second, they fail to adequately consider the importance information of each attribute in the node feature vector, leading to the loss of fine-grained information. Third, they insufficiently utilize node labels for GAD. To address these issues, this paper proposes a novel graph anomaly detection method called JPGFN (Feature Transformation Enhanced Jacobi Polynomial Graph Filtering Network). First, a Feature Separation Transformation Network (FSTNN) is developed to better learn fine-grained node features by feature separation and applying nonlinear transformations to node features across different dimensions. Second, an adaptive Jacobi polynomial graph filtering module is constructed based on Jacobi polynomials to adaptively capture complex frequency-domain features of graph signals. Finally, a node label constraint module is developed to facilitate the use of node labels and enhance the performance of GAD. Experimental results on multiple real-world datasets demonstrate that the proposed method significantly outperforms mainstream approaches.

发表机构

• School of Artificial Intelligence and Transportation Engineering, Fujian University of Technology（福建理工大学人工智能与交通工程学院）
• Institute of Artificial Intelligence, Fujian University of Technology（福建理工大学人工智能研究院）

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