Connectivity analysis of the global shipping network by eigenvalue decomposition

ABSTRACT

Maritime shipping necessitates flexible and cost-effective port access worldwide through the global shipping network. This paper presents an efficient method to identify major port communities, and analyses the network connectivity of the global shipping network based on community structure. The global shipping network is represented by a signless Laplacian matrix which can be decomposed to generate its eigenvectors and corresponding eigenvalues. The largest gaps between the eigenvalues were then used to determine the optimal number of communities within the network. The eigenvalue decomposition method offers the advantage of detecting port communities without relying on a priori assumption about the number of communities and the size of each community. By applying this method to a dataset collected from seven world leading liner shipping companies, we found that the ports are clustered into three communities in the global container shipping network, which is consistent with the major container trade routes. The sparse linkages between port communities indicate where access is relatively poor.

KEYWORDS:

• Shipping network
• eigenvalue decomposition
• community detection
• maritime connectivity

Acknowledgments

The authors would like to thank two anonymous reviewers and Dr. Zhijian Tony Li from USDA for their useful comments and suggestions on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Fujian Federation of Social Science Project [FJ2015C107, FJ2018C023], the National Natural Science Foundation of China [71602114], the Shanghai Science & Technology Committee Research Project [17040501700, 15590501700], and the Shanghai Special Research Project [17DZ2280200].