Edge conditional node update graph neural network for multivariate time series anomaly detection

초록

With the rapid advancement in cyber-physical systems the increasing number of sensors has significantly complicated manual monitoring of system states. Consequently graph-based time series anomaly detection methods have gained attention due to their ability to explicitly represent relationships between sensors. However these methods often apply a uniform source node representation across all connected target nodes even when updating different target node representations. Moreover the graph attention mechanism commonly used to infer unknown graph structures could constrain the diversity of source node representations. In this paper we introduce the Edge Conditional Node Update Graph Neural Network (ECNU-GNN). Our model equipped with an edge conditional node update module dynamically transforms source node representations based on connected edges to represent target nodes aptly. We validate performance on three real-world datasets: SWaT WADI and PSM. Our model demonstrates 5.4% 12.4% and 6.0% higher performance respectively compared to baseline models with best F1 scores. Our code is available at https://github.com/hayoung-jo/ECNU-GNN. © 2024 Elsevier Inc.

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