用时间点作节点，捕捉船舶行为异常的时空关系。
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
- 以时间戳为节点构建动态图，显式建模时序依赖
- 多船图保持稀疏性，有效捕捉空间交互模式
- 结合自编码与预测层，实现鲁棒异常检测
图神经网络在学习时空交互方面表现出强大能力，但传统方法通常依赖预定义图结构，可能掩盖实际关系。现有方法多基于固定空间位置定义节点，不适用于动态海事环境。本文提出一种新型图表示：将时间戳作为独立节点，通过图边显式捕获时序依赖。在此基础上构建多船图，有效建模空间交互同时保持图结构稀疏性。采用图卷积网络层提取时空特征，辅以预测层进行特征预测和变分图自编码器进行重构，实现对船舶行为异常的鲁棒检测。
原文摘要 · Abstract (English)
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.
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