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.

本研究解决了传统图学习方法在动态海洋环境中对节点和图结构固定定义的局限，提出了一种创新的图表示方式，将时间戳建模为独立节点，从而明确捕捉时间依赖关系。研究表明，该方法通过图卷积网络有效捕捉时空模式，提升了船舶行为异常检测的准确性与鲁棒性。

针对船舶行为异常的自适应稀疏图学习框架