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Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection
Li, Xiping Dong, Xiangyu Zhang, Xingyi Xie, Kun Feng, Yuanhao Wang, Bo Li, Guilin Zeng, Wuxiong Shu, Xiujun Wang, Sibo
Li, Xiping
Dong, Xiangyu
Zhang, Xingyi
Xie, Kun
Feng, Yuanhao
Wang, Bo
Li, Guilin
Zeng, Wuxiong
Shu, Xiujun
Wang, Sibo
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters.
Citation
X. Li et al., “Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection,” pp. 1565–1576, Aug. 2025, doi: 10.1145/3711896.3736877
Source
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Conference
2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Keywords
Chi-square Wavelet, Heterogeneous Graph Anomaly Detection, Spectral Graph Neural Networks, Distributed Computer Systems, Graph Neural Networks, Graph Theory, Heterogeneous Networks, Learning Systems, Semantics, Anomaly Detection, Chi-square Wavelet, Class Imbalance, Heterogeneous Graph, Heterogeneous Graph Anomaly Detection, High Frequency Hf, Key Issues, Neural Network Method, Spectral Graph Neural Network
Subjects
Source
2025 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher
Association for Computing Machinery