基于局部差分与全局谱融合的图异常检测

doi:10.16451/j.cnki.issn1003-6059.202507005

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3183 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Graph neural networks(GNNs) are widely recognized for their effectiveness in graph anomaly detection tasks. However, existing methods commonly struggle to identify camouflaged anomalies and exhibit poor performance under label scarcity. To address these issues, a method for graph anomaly detection based on local differential and global spectral fusion(GAD-LDSF) is proposed. Feature embeddings are employed in both spatial and spectral domains to enhance performance under camouflaged anomalies and label scarcity. First, probability-adaptive continuum embedding based on soft quantization is utilized to achieve data augmentation. A graph differential attention network is employed to accurately capture subtle differences between nodes and generate node representations in the spatial domain. Then, a globally homogenized dynamic spectral enhancement module is constructed. Chebyshev polynomials are leveraged to efficiently capture global anomaly signals and generate node representations in the spectral domain. Finally, spatial domain and spectral domain node representations are dynamically fused, and a predictor is employed to achieve anomalous node detection. Experiments on three benchmark datasets validate that GAD-LDSF achieves overall superior performance, particularly demonstrating its strong robustness and generalization in handling challenges such as camouflaged anomalies and label scarcity.

Key words： Graph Anomaly Detection Graph Representation Learning Graph Neural Networks Attention Mechanism Soft Quantization

Received: 29 April 2025

ZTFLH:

TP391

Fund:Supported by Postgraduate Scientific Research Innovation Project of People's Public Security University of China(No.2025YJSKY012), National Key Research and Development Program of China(No.2023YFC3321604

Corresponding Authors: LIU Zhao, Ph.D., associate professor. His research interests include machine learning and computer vision.

About author:: XU Dengbin, Master student. His research interests include machine learning and graph anomaly detection.YUAN Lining, Ph.D. candidate, senior engineer. His research interests include machine learning and graph neural network.WU Peichen, Master. His research inte-rests include machine learning and computer vision.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XU Dengbin
	YUAN Lining
	WU Peichen
	LIU Zhao

Cite this article:

XU Dengbin,YUAN Lining,WU Peichen等. Graph Anomaly Detection Based on Local Differential and Global Spectral Fusion[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(7): 641-654.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202507005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I7/641

[1] 胡威,李姝影,张鑫,等.基于图与语义表示学习的专利引文网络链路预测研究.数据分析与知识发现, 2024, 8(10): 28-43.
(HU W, LI S Y, ZHANG X, et al. Link Prediction in Patent Citation Networks Based on Graph and Semantic Representation Lear-ning. Data Analysis and Knowledge Discovery, 2024, 8(10): 28-43.)
[2] 代星月,叶海良,曹飞龙.社交影响增强的图神经网络推荐方法.模式识别与人工智能, 2024, 37(3): 221-230.
(DAI X Y, YE H L, CAO F L. Graph Neural Network Recommendation Based on Enhanced Social Influence. Pattern Recognition and Artificial Intelligence, 2024, 37(3): 221-230.)
[3] YING R, HE R N, CHEN K F, et al. Graph Convolutional Neural Networks for Web-Scale Recommender Systems // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 974-983.
[4] XIANG S, ZHU M Z, CHENG D W, et al. Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(12): 14557-14565.
[5] 陈波冯,李靖东,卢兴见,等.基于深度学习的图异常检测技术综述.计算机研究与发展, 2021, 58(7): 1436-1455.
(CHEN B F, LI J D, LU X J, et al. Survey of Deep Learning Based Graph Anomaly Detection Methods. Journal of Computer Research and Development, 2021, 58(7):1436-1455)
[6] GORI M, MONFARDINI G, SCARSELLI F. A New Model for Lear-ning in Graph Domains // Proc of the IEEE International Joint Conference on Neural Networks. Washington, USA: IEEE, 2005: 729-734.
[7] ZHANG S, TONG H H, XU J J, et al. Graph Convolutional Networks: A Comprehensive Review. Computational Social Networks, 2019, 6. DOI: 10.1186/s40649-019-0069-y.
[8] LIU Y, AO X, QIN Z D, et al. Pick and Choose: A GNN-Based Imbalanced Learning Approach for Fraud Detection // Proc of the Web Conference. New York, USA: ACM, 2021: 3168-3177.
[9] CHANG W J, YU J J, ZHOU X J. ConsE: Consistency Exploitation for Semi-supervised Anomaly Detection in Graphs // Proc of the International Joint Conference on Neural Networks. Washington, USA: IEEE, 2023. DOI: 10.1109/IJCNN54540.2023.10191356.
[10] HYUN W, LEE I, SUH B. LEX-GNN: Label-Exploring Graph Neu-ral Network for Accurate Fraud Detection // Proc of the 33rd ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2024: 3802-3806.
[11] DOU Y T, LIU Z W, SUN L, et al. Enhancing Graph Neural Network-Based Fraud Detectors Against Camouflaged Fraudsters // Proc of the 29th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2020: 315-324.
[12] LIU Z W, DOU Y T, YU P S, et al. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection // Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2020: 1569-1572.
[13] KIM H, CHOI J, WHANG J J. Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection // Proc of the IEEE International Conference on Data Mining Workshops. Washington, USA: IEEE, 2023: 1092-1096.
[14] ZHAO J, SHAO M L, TANG H L, et al. RHGNN: Fake Reviewer Detection Based on Reinforced Heterogeneous Graph Neural Networks. Knowledge-Based Systems, 2023, 280. DOI: 10.1016/j.knosys.2023.111029.
[15] TANG J H, LI J J, GAO Z Q, et al. Rethinking Graph Neural Networks for Anomaly Detection. Proceedings of Machine Learning Research, 2022, 162: 21076-21089.
[16] GAO Y, WANG X, HE X N, et al. Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum // Proc of the ACM Web Conference. New York, USA: ACM, 2023: 1528-1538.
[17] YE T Z, DONG L, XIA Y Q, et al. Differential Transformer[C/OL].[2025-03-21]. https://arxiv.org/pdf/2410.05258.
[18] VELIČKOVIČ P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C/OL].[2025-03-21]. https://arxiv.org/pdf/1710.10903.
[19] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering // Proc of the 30th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2016: 3844-3852.
[20] RAYANA S, AKOGLU L. Collective Opinion Spam Detection: Bri-dging Review Networks and Metadata // Proc of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2015: 985-994.
[21] WU B, YAO X Y, ZHANG B Y, et al. SplitGNN: Spectral Graph Neural Network for Fraud Detection Against Heterophily // Proc of the 32nd ACM International Conference on Information and Know-ledge Management. New York, USA: ACM, 2023: 2737-2746.
[22] LI Y Q, ZANG G S, SONG C Y, et al. A Universal Adaptive Algorithm for Graph Anomaly Detection. Information Processing and Management, 2025, 62(1). DOI: 10.1016/j.ipm.2024.103905.
[23] WANG X, DOU H, DONG D B, et al. Graph Anomaly Detection Based on Hybrid Node Representation Learning. Neural Networks, 2025, 185. DOI: 10.1016/j.neunet.2025.107169.