MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data
Summary: Introduces MH-GIN: learns per-attribute multi-scale temporal features and builds a multi-scale heterogeneous graph to capture inter-attribute dependencies caused by diverse update rates. Graph propagation yields ≈57% imputation error reduction vs SOTA while staying efficient. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Hengyu Liu
- 2. Tianyi Li
- 3. Yuqiang He
- 4. Kristian Torp
- 5. Yushuai Li
- 6. Christian S. Jensen
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,276 | Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series | 2020 | VLDB | 9.1261944e-05 |
| 4,332 | Missing Value Imputation on Multidimensional Time Series | 2021 | VLDB | 6.2805243e-05 |
| 6,004 | Compression of Uncertain Trajectories in Road Networks | 2020 | VLDB | 5.2415551e-05 |
| 6,900 | Kamel: A Scalable BERT-based System for Trajectory Imputation | 2024 | VLDB | 4.8925595e-05 |
| 9,446 | TRACE: Real-time Compression of Streaming Trajectories in Road Networks | 2021 | VLDB | 4.3404859e-05 |
| 10,750 | Quantifying Point Contributions: A Lightweight Framework for Efficient and Effective Query-Driven Trajectory Simplification | 2025 | VLDB | 4.1945683e-05 |
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