Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
This work addresses the problem of anomaly detection in non-grid spatio-temporal systems like maritime traffic for researchers, though it is incremental as it focuses on creating a benchmark rather than a new detection method.
The authors tackled the challenge of anomaly detection in maritime traffic, where spatio-temporal graphs lack fixed nodes, by introducing a novel benchmark dataset that extends OMTAD to evaluate anomalies at node, edge, and graph levels, using LLM-based agents for data synthesis and anomaly injection.
Spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in structured domains such as road traffic and public transportation, where spatial entities can be naturally represented as fixed nodes. In contrast, many real-world systems including maritime traffic lack such fixed anchors, making the construction of spatio-temporal graphs a fundamental challenge. Anomaly detection in these non-grid environments is particularly difficult due to the absence of canonical reference points, the sparsity and irregularity of trajectories, and the fact that anomalies may manifest at multiple granularities. In this work, we introduce a novel benchmark dataset for anomaly detection in the maritime domain, extending the Open Maritime Traffic Analysis Dataset (OMTAD) into a benchmark tailored for graph-based anomaly detection. Our dataset enables systematic evaluation across three different granularities: node-level, edge-level, and graph-level anomalies. We plan to employ two specialized LLM-based agents: \emph{Trajectory Synthesizer} and \emph{Anomaly Injector} to construct richer interaction contexts and generate semantically meaningful anomalies. We expect this benchmark to promote reproducibility and to foster methodological advances in anomaly detection for non-grid spatio-temporal systems.