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Link prediction of the world container shipping network: A network structure perspective
Jiawei Ge1, Xuefeng Wang1, Wenming Shi2
1College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China.
Chaos (Woodbury, N.Y.)
|December 9, 2021
Summary
This study reveals the evolutionary patterns of the world container shipping network (WCSN) using network hierarchy analysis. Findings show trade patterns influence network structure, with KS-LRW predicting future connectivity improvements for shipping companies.
Area of Science:
- Maritime Logistics
- Network Science
- Transportation Systems Analysis
Background:
- The world container shipping network (WCSN) exhibits increasing complexity, yet its evolutionary mechanisms are not fully understood.
- Existing link prediction models require advancement to accurately capture the dynamic nature of global shipping routes.
Purpose of the Study:
- To explore the evolutionary patterns of the WCSN by enhancing existing link prediction models.
- To identify key drivers of network hierarchy and predict future network development.
Main Methods:
- Applied k-shell decomposition to analyze network hierarchy.
- Evaluated network structure using four indices: KS-Salton, KS-AA, KS-RA, and KS-LRW.
- Utilized the best-performing KS-LRW index for future WCSN simulation and potential edge prediction.
Main Results:
- Network hierarchy is significantly influenced by global trade patterns and exhibits distinct geographic characteristics.
- The KS-LRW index demonstrated superior performance in evaluating network hierarchy.
- Prediction of 1677 potential future edges for the WCSN indicates enhanced overall network connectivity and efficiency.
Conclusions:
- Understanding WCSN evolution through network hierarchy analysis is crucial for optimizing shipping logistics.
- The KS-LRW index provides a robust method for predicting future network links and improving shipping efficiency.
- Findings offer strategic insights for shipping companies to reduce operational costs by analyzing network data for new route development.
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