A study of the temporal robustness of the growing global container-shipping network

Nuo Wang,Nuan Wu,Hua-Kun Yan,Di Wu,Ling-Ling Dong

doi:10.1038/srep34217

Nuo Wang, Nuan Wu + Show 3 more

Open Access

https://doi.org/10.1038/srep34217

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Abstract

Whether they thrive as they grow must be determined for all constantly expanding networks. However, few studies have focused on this important network feature or the development of quantitative analytical methods. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. As an example, we collected container liner companies’ data at two time points (2004 and 2014) and built a shipping network with ports as nodes and routes as links. We thus obtained a quantitative value of the temporal robustness. The temporal robustness is a significant network property because, for the first time, we can clearly recognize that the shipping network has become more vulnerable to damage over the last decade: When the node failure scale reached 50% of the entire network, the temporal robustness was approximately −0.51% for random errors and −12.63% for intentional attacks. The proposed concept and analytical method described in this paper are significant for other network studies.

Highlights

Whether they thrive as they grow must be determined for all constantly expanding networks
Based on the proposed problem and the actual demand, we introduce the concept of network temporal robustness and a quantitative analytical method
The results show the network temporal robustness decreased in both cases, and the decrease in the intentional attack case was more drastic

Summary

Introduction

Whether they thrive as they grow must be determined for all constantly expanding networks. Given the formation and growth of the global container-shipping network, we proposed the concept of network temporal robustness and quantitative method. Several researchers have addressed the basic characteristics and metrics of complex networks in terms of the probability distribution of node degrees and network connectivity[12,13,14,15], the quantitative assessment of network robustness and vulnerability[16,17,18], a network’s recovery ability and strategy after partial failure[6,19,20], the improvement of network survivability[15,21,22,23], and the evaluation of network efficiency and simulation-based analytical methods[24,25,26]. To observe an important objective-property of a network as it grows, we establish a new concept of network’s temporal robustness and a general analytical method. Because the global economy relies heavily on the container-shipping network, when unexpected events

Methods

Results

Conclusion