Empirical analysis of the worldwide maritime transportation network
By Yihong Hu, Daoli Zhu. 2008
250 citations recorded by the source corpus.
In this paper we present an empirical study of the worldwide maritime transportation network (WMN) in which the nodes are ports and links are container liners connecting the ports. Using the different representations of network topology — the spaces L and P, we study the statistical properties of WMN including degree distribution, degree correlations, weight distribution, strength distribution, average shortest path length, line length distribution and centrality measures. We find that WMN is a small-world network with power law behavior. Important nodes are identified based on different centrality measures. Through analyzing weighted clustering coefficient and weighted average nearest neighbors degree, we reveal the hierarchy structure and rich-club phenomenon in the network.
Subfields: Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/ce164a0eafa25a0f50fdd5ce10408086d648a519
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In Statistical and Nonlinear Physics
- Jianxi Gao
- Marc Barthelemy
- Cesar Ducruet
- L. Beauguitte
- Boleslaw K. Szymanski
- H. Eugene Stanley
- José J. Ramasco
- Filippo Simini
Citations in the corpus
- Network resilience (2020, background): “railway networks [588], the worldwide maritime transportation network [589], and power grid [590].”
- Spatial Science and Network Science: Review and Outcomes of a Complex Relationship (2014, background): “…networks do not specifically measure their spatial dimension, as they only look at their topological dimension as in the case of maritime networks (Hu and Zhu, 2009) and commuter flows (De Montis et al., 2010; Reggiani et al., 2011b; Caschili and De Montis, 2013) where distance parameters are not…”
- Human mobility: Models and applications (2017, background): “The centrality of ports was studied in [309], where it was found that Singapore, Antwerp, Bushan and Rotterdam top the list in terms of both traffic and load.”