title: A study of the temporal robustness of the growing global container-shipping network.
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.
authors.affiliation: Department of Transportation Management, Dalian Maritime University, Dalian 116026, China.
authors.forename: Nuo
authors.initials: N
authors.lastname: Wang
authors.affiliation: Department of Transportation Management, Dalian Maritime University, Dalian 116026, China.
authors.forename: Nuan
authors.initials: N
authors.lastname: Wu
authors.affiliation: Department of Transportation Management, Dalian Maritime University, Dalian 116026, China.
authors.forename: Ling-Ling
authors.initials: LL
authors.lastname: Dong
authors.affiliation: Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China.
authors.forename: Hua-Kun
authors.initials: HK
authors.lastname: Yan
authors.affiliation: Department of Transportation Management, Dalian Maritime University, Dalian 116026, China.
authors.forename: Di
authors.initials: D
authors.lastname: Wu