Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910836813987840 |
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| author | Kim, Jeehong Kim, Minchan Ju, Jaeseong Hwang, Youngseok Lee, Wonhee Park, Hyunwoo |
| author_facet | Kim, Jeehong Kim, Minchan Ju, Jaeseong Hwang, Youngseok Lee, Wonhee Park, Hyunwoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies Kim, Jeehong Kim, Minchan Ju, Jaeseong Hwang, Youngseok Lee, Wonhee Park, Hyunwoo Machine Learning Artificial Intelligence 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. |
| title | Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.14197 |