An eigenvector centrality analysis of world container shipping network connectivity. (August 2020)

• Record Type: Journal Article
• Authors: Cheung, Kam-Fung; Bell, Michael G.H.; Pan, Jing-Jing; Perera, Supun
• Is Part Of: Transportation research. Volume 140(2020)
• Publication Date: 2020-08
• Subjects: Shipping network connectivity -- Eigenvector centrality -- Largest eigenvalue -- Asymmetric adjacency matrix; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2020.101991
• Languages: English
• ISSNs: 1366-5545

Abstract

Highlights: The largest eigenvalue of the weighted adjacency matrix measures network connectivity. Adding a link always increases the largest eigenvalue of the adjacency matrix. A max-min integer optimization model is proposed to maximize the largest eigenvalue. Results for the world container shipping network are presented. First to use the eigenvector centrality to improve shipping network connectivity. Abstract: Container shipping accounts for most of the world merchandise trade. Better maritime connectivity leads to lower freight rates and greater economic growth. This paper presents a novel max–min integer optimization model to facilitate better shipping network connectivity by analysing the largest eigenvalue and its corresponding eigenvector of the (asymmetric) frequency weighted adjacency matrix. An algorithm is presented that can quickly identify which link not currently in the container shipping network would best improve its connectivity. A demand matrix is not required by this method of analysis and network symmetry is not assumed. The method could strengthen direct connection between port pairs.