Link Prediction Method of LNG Shipping Network Based on Multi-index Coupling Learning
YU Hongchua,b,c, CHEN Fenga, ZHANG Pinga
a. School of Navigation; b. State Key Laboratory of Maritime Technology and Safety,Wuhan University of Technology, Wuhan 430063, China; c. Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572025, China
Abstract：This paper focuses on the global LNG trade network from 2013 to 2022, aiming to identify potential LNG trade relationships through an improved link prediction algorithm. It is important to assist LNG-consuming countries in finding potential partners and ensuring energy supply security. External factors, such as geographical distance between nodes, trade volumes and poli-tical stability of countries, are fully considered, and these are coupled with internal network indices through a multi-index approach. A random forest classifier is used to enhance prediction accuracy. The results show that the average prediction accuracy can reach 97.55%. And, 120 potential links had been successfully identified, of which 99 was transformed into real links in 2022, and 94 links were transformed into real links within following three years. These results not only confirm the high precision of the algorithm in forecasting unknown links but also highlight its effective capacity for identifying potential future links.
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