Edge Conditional Node Update Graph Neural Network for Multi-variate Time
  Series Anomaly Detection
Hayoung Jo Seong-Whan Lee
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 best F1 baseline models.