MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)

TL;DR

This paper introduces MH-GIN, a novel graph-based neural network that effectively captures multi-scale dependencies among heterogeneous attributes in AIS data to improve missing value imputation accuracy.

Contribution

The paper presents MH-GIN, a multi-scale heterogeneous graph neural network designed to enhance imputation accuracy for AIS data by modeling complex attribute dependencies.

Findings

Achieves 57% reduction in imputation errors compared to state-of-the-art methods.

Effectively captures multi-scale dependencies among heterogeneous attributes.

Maintains computational efficiency while improving accuracy.

Abstract

Location-tracking data from the Automatic Identification System, much of which is publicly available, plays a key role in a range of maritime safety and monitoring applications. However, the data suffers from missing values that hamper downstream applications. Imputing the missing values is challenging because the values of different heterogeneous attributes are updated at diverse rates, resulting in the occurrence of multi-scale dependencies among attributes. Existing imputation methods that assume similar update rates across attributes are unable to capture and exploit such dependencies, limiting their imputation accuracy. We propose MH-GIN, a Multi-scale Heterogeneous Graph-based Imputation Network that aims improve imputation accuracy by capturing multi-scale dependencies. Specifically, MH-GIN first extracts multi-scale temporal features for each attribute while preserving their intrinsic heterogeneous characteristics. Then, it constructs a multi-scale heterogeneous graph to explicitly model dependencies between heterogeneous attributes to enable more accurate imputation of missing values through graph propagation. Experimental results on two real-world datasets find that MH-GIN is capable of an average 57% reduction in imputation errors compared to state-of-the-art methods, while maintaining computational efficiency. The source code and implementation details of MH-GIN are publicly available https://github.com/hyLiu1994/MH-GIN.