Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection

2026-08-27

AI Summary

本文针对图异常检测中的特征适应性、细粒度信息丢失及标签利用不足问题，提出了一种结合特征变换与自适应Jacobi多项式图滤波的新方法。

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 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.

Problem

• graph anomaly detection
• frequency-domain filtering
• node feature vector
• node labels

Innovation

• Feature Separation Transformation Network
• Adaptive Jacobi Polynomial Graph Filtering
• Node Label Constraint