MFAD: a graph anomaly detection framework based on multi-frequency reconstruction

Senbao Hou; Enguang Zuo; Jie Zhong; Junyi Yan; Ziwei Yan; Tianle Li; Xiaoyi Lv

doi:10.1117/12.3006042

10 October 2023 MFAD: a graph anomaly detection framework based on multi-frequency reconstruction

Senbao Hou, Enguang Zuo, Jie Zhong, Junyi Yan, Ziwei Yan, Tianle Li, Xiaoyi Lv

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279957 (2023) https://doi.org/10.1117/12.3006042
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Graph anomaly detection in graph data has received significant attention due to its practical significance in various vital applications such as network security, finance, and social networks. The current mainstream approach for attribute graph anomaly detection is based on contrastive learning using graph neural networks, which only consider homogeneous low-frequency signals. However, in attribute networks, normal and anomalous nodes exhibit different frequency patterns. This motivates the proposal of a graph anomaly detection framework based on multi-frequency reconstruction to capture the signal patterns of anomaly. Specifically, our method constructs multiple filters based on target nodes and utilizes two modules, namely, low-frequency reconstruction and contrastive learning, for anomaly detection. The generative low-frequency reconstruction module enables us to capture anomalies in the high-frequency attribute space, while the contrastive learning module leverages richer structural information from multiple subgraphs to capture anomalies in the structural and mixed spaces. We conducted extensive experiments on five publicly available datasets, demonstrating that our method significantly outperforms state-of-the-art approaches.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Senbao Hou, Enguang Zuo, Jie Zhong, Junyi Yan, Ziwei Yan, Tianle Li, and Xiaoyi Lv "MFAD: a graph anomaly detection framework based on multi-frequency reconstruction", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279957 (10 October 2023); https://doi.org/10.1117/12.3006042

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KEYWORDS

Tunable filters

Data modeling

Social networks

Detection theory

Digital filtering

Linear filtering

Matrices

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