Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies

• URL: http://arxiv.org/abs/2502.14197v1
• Date: Thu, 20 Feb 2025 02:01:40 GMT
• Title: Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
• Authors: Jeehong Kim, Minchan Kim, Jaeseong Ju, Youngseok Hwang, Wonhee Lee, Hyunwoo Park,
• Abstract summary: Graph neural networks have emerged as a powerful tool for learning precise interactions. Our method introduces an innovative graph representation where edges are modeled as timestamp nodes. This setup is extended to construct a multi-ship graph that captures spatial interactions while preserving graph sparsity.
• Score: 3.3711670942444014
• License:
• 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.

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