Detecting emerging ties in shipping network is essential for capturing changes in global trade patterns and enhancing supply chain resilience. This study proposes a multi-indicator coupled link prediction framework integrating network topology, node attributes, and external features to accurately predict potential links in the Liquedied Natural Gas (LNG) shipping network. As a case study of the LNG shipping network from 2018 to 2023, the results show that the method can effectively identify potential LNG shipping links, with a six-year mean Area under the curve (AUC) of 98.19%, a six-year mean accuracy of 98.13%, and a six-year mean precision of 97.3%. The detected emerging transportation ties reveal that LNG shipping is moving a pattern of diversified supply sources, concentrated demand, and strong cross-regional connectivity. These findings provide support for LNG route planning, hub construction, supply-chain risk management, and energy security enhancement.
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Yu et al. (2026) studied this question.
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