TS-GNN: A Temporal-Spatial Graph Neural Network for Anomaly Detection in Multiplex Industrial Information Services

The increasing complexity of industrial information service systems presents significant challenges for anomaly detection, particularly in ensuring service reliability across multilayered service networks. This paper proposes TS-GNN, a novel Temporal-Spatial Graph Neural Network framework that effectively integrates temporal pattern recognition with spatial dependency modeling for anomaly detection in industrial service environments. The framework employs a multi-scale temporal feature extraction mechanism that combines frequency-domain transformation with hierarchical decomposition to capture temporal patterns at different granularities. Subsequently, a graph neural network with attention-based message passing models spatial correlations and anomaly propagation patterns among service nodes. Comprehensive experiments on three benchmark datasets from service computing domains demonstrate that TSGNN achieves superior performance, with F1-scores of 94.74% on SWaT, 85.14% on SMD, and 96.36% on PSM datasets. Compared to the best baseline methods, TS-GNN shows consistent improvements with an average $\mathbf{F 1}$-score enhancement of $\mathbf{0. 7 9 \%}$ points, providing an effective solution for enhancing the reliability and robustness of service computing systems.