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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)

Paper ID
14309
Venue
VLDB
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,272 | 28.54%
DOI
10.14778/3773749.3773756

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