Modular gateway-ness connectivity and structural core organization in maritime network science.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
05 06 2020
05 06 2020
Historique:
received:
28
09
2018
accepted:
20
04
2020
entrez:
7
6
2020
pubmed:
7
6
2020
medline:
7
6
2020
Statut:
epublish
Résumé
Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shipping routes, we need to understand the current ones and their complex systems association with international trade. We investigate the structure of the global liner shipping network (GLSN), finding it is an economic small-world network with a trade-off between high transportation efficiency and low wiring cost. To enhance understanding of this trade-off, we examine the modular segregation of the GLSN; we study provincial-, connector-hub ports and propose the definition of gateway-hub ports, using three respective structural measures. The gateway-hub structural-core organization seems a salient property of the GLSN, which proves importantly associated to network integration and function in realizing the cargo transportation of international trade. This finding offers new insights into the GLSN's structural organization complexity and its relevance to international trade.
Identifiants
pubmed: 32503974
doi: 10.1038/s41467-020-16619-5
pii: 10.1038/s41467-020-16619-5
pmc: PMC7275034
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2849Références
UNCTAD. Review of Maritime Transport 2017. https://unctad.org/en/pages/publicationwebflyer.aspx?publicationid=1890 (2017).
Bernhofen, D. M., El-Sahli, Z. & Kneller, R. Estimating the effects of the container revolution on world trade. J. Int. Econ. 98, 36–50 (2016).
doi: 10.1016/j.jinteco.2015.09.001
Limão, N. & Venables, A. J. Infrastructure, geographical disadvantage, transport costs, and trade. World Bank Econ. Rev. 15, 451–479 (2001).
doi: 10.1093/wber/15.3.451
Clark, X., Dollar, D. & Micco, A. Port efficiency, maritime transport costs, and bilateral trade. J. Dev. Econ. 75, 417–450 (2004).
doi: 10.1016/j.jdeveco.2004.06.005
Bar-Yam, Y. Dynamics of Complex Systems. (Westview Press, Cambridge, MA, 1997).
Newman, M. E. J. The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003).
doi: 10.1137/S003614450342480
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
pubmed: 9623998
doi: 10.1038/30918
Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).
pubmed: 10521342
doi: 10.1126/science.286.5439.509
Borgatti, S. P., Mehra, A., Brass, D. J. & Labianca, G. Network analysis in the social sciences. Science 323, 892–895 (2009).
pubmed: 19213908
doi: 10.1126/science.1165821
Ducruet, C. & Notteboom, T. The worldwide maritime network of container shipping: spatial structure and regional dynamics. Glob. Netw. 12, 395–423 (2012).
doi: 10.1111/j.1471-0374.2011.00355.x
Kaluza, P., Kölzsch, A., Gastner, M. T. & Blasius, B. The complex network of global cargo ship movements. J. R. Soc. Interface 7, 1093–1103 (2010).
pubmed: 20086053
pmcid: 2880080
doi: 10.1098/rsif.2009.0495
Wang, N., Wu, N., Dong, L., Yan, H. & Wu, D. A study of the temporal robustness of the growing global container-shipping network. Sci. Rep. 6, 34217 (2016).
pubmed: 27713549
pmcid: 5054361
doi: 10.1038/srep34217
Hu, Y. & Zhu, D. Empirical analysis of the worldwide maritime transportation network. Phys. A Stat. Mech. Appl. 388, 2061–2071 (2009).
doi: 10.1016/j.physa.2008.12.016
Deng, W.-B., Guo, L., Li, W. & Cai, X. Worldwide marine transportation network: efficiency and container throughput. Chin. Phys. Lett. 26, 242–245 (2009).
Gastner, M. T. & Ducruet, C. The distribution functions of vessel calls and port connectivity in the global cargo ship network. Maritime Networks: Spatial Structures and Time Dynamics 242–261. (Routledge, New York, NY, 2015).
Alumur, S. & Kara, B. Y. Network hub location problems: the state of the art. Eur. J. Oper. Res. 190, 1–21 (2008).
doi: 10.1016/j.ejor.2007.06.008
Gelareh, S., Nickel, S. & Pisinger, D. Liner shipping hub network design in a competitive environment. Transp. Res. Part E Logist. Transp. Rev. 46, 991–1004 (2010).
doi: 10.1016/j.tre.2010.05.005
Zheng, J., Meng, Q. & Sun, Z. Liner hub-and-spoke shipping network design. Transp. Res. Part E Logist. Transp. Rev. 75, 32–48 (2015).
doi: 10.1016/j.tre.2014.12.014
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
doi: 10.1103/PhysRevE.69.026113
Sporns, O. Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162–171 (2013).
pubmed: 23294553
doi: 10.1016/j.conb.2012.11.015
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015).
pubmed: 26081790
doi: 10.1038/nrn3963
Latora, V. & Marchiori, M. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701 (2001).
pubmed: 11690461
doi: 10.1103/PhysRevLett.87.198701
Latora, V. & Marchiori, M. Economic small-world behavior in weighted networks. Eur. Phys. J. B - Condens. Matter Complex Syst. 32, 249–263 (2003).
doi: 10.1140/epjb/e2003-00095-5
Freeman, L. C. A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977).
doi: 10.2307/3033543
Guimerà, R., Mossa, S., Turtschi, A. & Amaral, L. A. N. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc. Natl Acad. Sci. USA 102, 7794–7799 (2005).
pubmed: 15911778
doi: 10.1073/pnas.0407994102
Wang, J., Mo, H., Wang, F. & Jin, F. Exploring the network structure and nodal centrality of China’s air transport network: a complex network approach. J. Transp. Geogr. 19, 712–721 (2011).
doi: 10.1016/j.jtrangeo.2010.08.012
Freeman, L. C. Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1978).
doi: 10.1016/0378-8733(78)90021-7
Newman, M. E. J. Assortative mixing in networks. Phys. Rev. Lett. 89, 2087011–2087014 (2002).
Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep. 3, 1613 (2013).
pubmed: 23563395
pmcid: 3619147
doi: 10.1038/srep01613
Humphries, M. D. & Gurney, K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051 (2008).
pubmed: 18446219
doi: 10.1371/journal.pone.0002051
Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H. & Laurienti, P. J. The ubiquity of small-world networks. Brain Connect. 1, 367–375 (2011).
pubmed: 22432451
pmcid: 3604768
doi: 10.1089/brain.2011.0038
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).
pubmed: 16723398
doi: 10.1073/pnas.0601602103
Meunier, D., Lambiotte, R. & Bullmore, E. T. Modular and hierarchically modular organization of brain networks. Front. Neurosci. 4, 200 (2010).
pubmed: 21151783
pmcid: 3000003
doi: 10.3389/fnins.2010.00200
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
Fortunato, S. & Hric, D. Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016).
doi: 10.1016/j.physrep.2016.09.002
Kim, S. & Shin, E.-H. A longitudinal analysis of globalization and regionalization in international trade: a social network approach. Soc. Forces 81, 445–468 (2002).
doi: 10.1353/sof.2003.0014
Guimerà, R. & Amaral, L. A. N. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).
pubmed: 2175124
pmcid: 2175124
doi: 10.1038/nature03288
Fujita, M., Krugman, P. R. & Venables, A. J. The Spatial Economy: Cities, Regions, and International Trade. (The MIT Press, Cambridge, MA, 2001).
Lee, S. H., Cucuringu, M. & Porter, M. A. Density-based and transport-based core-periphery structures in networks. Phys. Rev. E 89, 032810 (2014).
doi: 10.1103/PhysRevE.89.032810
Barrat, A., Barthélemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl Acad. Sci. USA 101, 3747–3752 (2004).
pubmed: 15007165
doi: 10.1073/pnas.0400087101
Borgatti, S. P. & Halgin, D. S. On network theory. Organ. Sci. 22, 1168–1181 (2011).
doi: 10.1287/orsc.1100.0641
Burt, R. S. Structural Holes: The Social Structure of Competition. (Harvard University Press, Cambridge, MA, 1992).
Zhou, S. & Mondragón, R. J. The rich-club phenomenon in the internet topology. IEEE Commun. Lett. 8, 180–182 (2004).
doi: 10.1109/LCOMM.2004.823426
Colizza, V., Flammini, A., Serrano, M. A. & Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110–115 (2006).
doi: 10.1038/nphys209
Muscoloni, A. & Cannistraci, C. V. Rich-clubness test: how to determine whether a complex network has or doesn’t have a rich-club? Preprint at https://arxiv.org/abs/1704.03526 (2017).
Guimerà, R. & Amaral, L. A. N. Cartography of complex networks: modules and universal roles. J. Stat. Mech. Theory Exp. 2005, P02001 (2005).
doi: 10.1088/1742-5468/2005/02/P02001
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D.-U. Complex networks: Structure and dynamics. Phys. Rep. 424, 175–308 (2006).
doi: 10.1016/j.physrep.2005.10.009
Chang, X., Xu, T., Li, Y. & Wang, K. Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of ‘date’ and ‘party’ hubs. Sci. Rep. 3, 1691 (2013).
pubmed: 23603706
pmcid: 3631766
doi: 10.1038/srep01691
Sporns, O., Honey, C. J. & Kötter, R. Identification and classification of hubs in brain networks. PLoS ONE 2, e1049 (2007).
pubmed: 17940613
pmcid: 2013941
doi: 10.1371/journal.pone.0001049
Goodwin, P. B. Empirical evidence on induced traffic. Transportation 23, 35–54 (1996).
doi: 10.1007/BF00166218
Sienkiewicz, J. & Hołyst, J. A. Statistical analysis of 22 public transport networks in Poland. Phys. Rev. E 72, 046127 (2005).
doi: 10.1103/PhysRevE.72.046127
Sen, P. et al. Small-world properties of the Indian railway network. Phys. Rev. E 67, 036106 (2003).
doi: 10.1103/PhysRevE.67.036106
Kojaku, S., Xu, M., Xia, H. & Masuda, N. Multiscale core-periphery structure in a global liner shipping network. Sci. Rep. 9, 404 (2019).
pubmed: 30674915
pmcid: 6344524
doi: 10.1038/s41598-018-35922-2
Daminelli, S., Thomas, J. M., Durán, C. & Cannistraci, C. V. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. N. J. Phys. 17, 113037 (2015).
doi: 10.1088/1367-2630/17/11/113037
Narula, V., Zippo, A. G., Muscoloni, A., Biella, G. E. M. & Cannistraci, C. V. Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain? Appl. Netw. Sci. 2, 28 (2017).
pubmed: 30443582
pmcid: 6214247
doi: 10.1007/s41109-017-0048-x
Danon, L., Díaz-Guilera, A., Duch, J. & Arenas, A. Comparing community structure identification. J. Stat. Mech. Theory Exp. 2005, P09008 (2005).
doi: 10.1088/1742-5468/2005/09/P09008
Muscoloni, A., Thomas, J. M., Ciucci, S., Bianconi, G. & Cannistraci, C. V. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nat. Commun. 8, 1615 (2017).
pubmed: 29151574
pmcid: 5694768
doi: 10.1038/s41467-017-01825-5
Muscoloni, A. & Cannistraci, C. V. Navigability evaluation of complex networks by greedy routing efficiency. Proc. Natl Acad. Sci. USA 116, 1468–1469 (2019).
pubmed: 30630863
doi: 10.1073/pnas.1817880116
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
doi: 10.1137/070710111
Voitalov, I., van der Hoorn, P., van der Hofstad, R. & Krioukov, D. Scale-free networks well done. Phys. Rev. Res. 1, 033034 (2019).
doi: 10.1103/PhysRevResearch.1.033034
Muscoloni, A. & Cannistraci, C. V. Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding. Preprint at https://arxiv.org/abs/1907.00025 (2019).