Authors
Zekang Li, Ruonan Liu, Dongyue Chen, Qinghua Hu
Published in
IEEE transactions on neural networks and learning systems. Volume PP. May 21, 2025. Epub May 21, 2025.
Abstract
Graph node anomaly detection has important applications in practical scenarios. Although many graph neural networks (GNNs) have been proposed, how to design tailored spectral filters for node anomaly detection to fully mine high-frequency signals in the graph is still a challenge. Most GNNs are equivalent to low-pass filters and mine multiorder signals through a series structure. The computational cost increases as the number of layers increases and further leads to an over-smoothing problem. They mainly focus on low-frequency signals and suppress high-frequency signals, thus smoothing the differences between abnormal and normal nodes, making them indistinguishable. Due to the difficulty in mining high-frequency signals, the poorly distinguishable feature representations learned by low-pass GNNs can even harm the performance of data augmentation. To solve the above challenges, in this article, we propose a or-gate mixup multiscale spectral GNN (MMGNN) from the spectral domain. Specifically, we design multiorder multiscale bandpass filters through the superposition of polynomial spectral filters and then decompose them into preprocessing parts and training parts to form a double-parallel structure, which can effectively mine high-frequency signals in the graph and reduce computational cost. Finally, we propose or-gate mixup to perform data augmentation in the spectral space to improve model generalization. Experimental results on four real-world datasets demonstrate the effectiveness of the proposed MMGNN against the state-of-the-art methods.
PMID:
40397640
Bibliographic data and abstract were imported from PubMed on 22 May 2025.
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