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SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
Dong, Xiangyu Zhang, Xingyi Sun, Yanni Chen, Lei Yuan, Mingxuan Wang, Sibo
Dong, Xiangyu
Zhang, Xingyi
Sun, Yanni
Chen, Lei
Yuan, Mingxuan
Wang, Sibo
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
The smoothing issue in graph learning leads to indistinguishable node representations, posing significant challenges for graph-related tasks. However, our experiments reveal that this problem can uncover underlying properties of node anomaly detection (NAD) that previous research has missed. We introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP), which indicate that the representations of anomalous nodes are harder to smooth than those of normal ones. In addition, we explore the theoretical implications of these patterns, demonstrating the potential benefits of ISP and NSP for NAD tasks. Motivated by these findings, we propose SmoothGNN, a novel unsupervised NAD framework. First, we design a learning component to explicitly capture ISP for detecting node anomalies. Second, we design a spectral graph neural network to implicitly learn ISP to enhance detection. Third, we design an effective coefficient based on our findings that NSP can serve as coefficients for node representations, aiding in the identification of anomalous nodes. Furthermore, we devise a novel anomaly measure to calculate loss functions and anomalous scores for nodes, reflecting the properties of NAD using ISP and NSP. Extensive experiments on 9 real datasets show that SmoothGNN outperforms the best rival by an average of 14.66% in AUC and 7.28% in Average Precision, with 75x running time speedup, validating the effectiveness and efficiency of our framework. © 2025 Copyright held by the owner/author(s).
Citation
X. Dong, X. Zhang, Y. Sun, L. Chen, M. Yuan, and S. Wang, “SmoothGNN: Smoothing-based GNN for Unsupervised Node Anomaly Detection,” May 2024, doi: 10.1145/3696410.3714615.
Source
WWW 2025 - Proceedings of the ACM Web Conference
Conference
34th ACM Web Conference, WWW 2025
Keywords
Smoothing Patterns, Unsupervised Node Anomaly Detection
Subjects
Source
34th ACM Web Conference, WWW 2025
Publisher
Association for Computing Machinery