[1]WANG TianHao,DOU Hao,WANG Xiang,et al.Graph anomaly detection method integrating graph filtering and an attention mechanism[J].CAAI Transactions on Intelligent Systems,2026,21(4):919-931.[doi:10.11992/tis.202510033]
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Graph anomaly detection method integrating graph filtering and an attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 919-931 Column: 学术论文—机器学习 Public date: 2026-07-05
Title:
Graph anomaly detection method integrating graph filtering and an attention mechanism
Author(s):
WANG TianHao1;  DOU Hao1;  WANG Xiang1; 2;  MAO Guojun1; 2
1. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China;
2. Fujian Provincial Key Laboratory of Big Data Mining and Application, Fujian University of Technology, Fuzhou 350118, China
Keywords:
graph data; graph neural networks; anomaly detection; heterophilic connection; feature inconsistency; graph filtering; attention mechanism; frequency domain information fusion
CLC:
TP391
DOI:
10.11992/tis.202510033
Abstract:
To address the pervasive issues of heterophilic connections and feature inconsistency in graph neural network–based anomaly detection, this paper proposes the flexible high–low-frequency and attention neural network (FHANN), a graph anomaly detection method that integrates graph filtering and attention mechanisms. Specifically, to mitigate the effects of heterophilic connections, a graph filtering module is designed to adaptively capture and fuse high- and low-frequency signals for node representation learning. To address feature inconsistency, a graph attention module is introduced to learn the importance of node features and generate node representations based on feature similarity. The representations produced by the two modules are then fused for anomaly detection. Experiments on three real-world graph anomaly detection datasets demonstrate that FHANN achieves average improvements of 1.17% and 4.25% in area under the receiver operating characteristic curve(AUC-ROC) and area under the precision-recall curve(AUC-PR), respectively. These results verify the effectiveness of the proposed method and provide valuable insights for anomaly detection in complex graph-structured data, including social network analysis and financial fraud detection.
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