Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies

TL;DR

This paper proposes an adaptive graph learning framework that models timestamps as nodes to better capture spatiotemporal dependencies in maritime vessel behavior, improving anomaly detection accuracy.

Contribution

It introduces a novel graph representation with timestamp nodes and a multi-ship graph for dynamic maritime environments, enhancing spatiotemporal modeling capabilities.

Findings

Effective detection of vessel behavior anomalies

Improved modeling of temporal dependencies

Robustness in dynamic maritime scenarios

Abstract

Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure the precise relationships being modeled. Additionally, existing methods typically define nodes based on fixed spatial locations, a strategy that is ill-suited for dynamic environments like maritime environments. Our method introduces an innovative graph representation where timestamps are modeled as distinct nodes, allowing temporal dependencies to be explicitly captured through graph edges. This setup is extended to construct a multi-ship graph that effectively captures spatial interactions while preserving graph sparsity. The graph is processed using Graph Convolutional Network layers to capture spatiotemporal patterns, with a forecasting layer for feature prediction and a Variational Graph Autoencoder for reconstruction, enabling robust anomaly detection.