Abstract
Transportation networks play a crucial role nowadays, not only in positive terms that include human mobility and the exchange of goods, but also in negative terms that include the spread of diseases and other malignancies. Among these networks, the worldwide aviation network (WAN) has become the largest and one of the most important global transportation networks in modern society, supporting the traffic of billions of passengers traveling between thousands of airports on millions of flights every year. Since the WAN has become one of the indispensable infrastructures in our daily lives, understanding its structure and vulnerability is an essential issue. In this work, we apply complex network analyses to elucidate the hidden characteristics of the network. We first construct a global aviation network using datasets obtained from an open source project named OpenFlights. We then clarify the topological and spatial characteristics of the constructed network and provide a binary status model to investigate the dynamics of the network with emphasis on its vulnerability as a result of extreme events. Our results may contribute to the understanding of the response of the network to disturbances and provide insights on the construction of a robust network and the improvement of the network resilience.
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References
Albert R, Barabási A (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47
Albert R, Jeong H, Barabási A (2000) Error and attack tolerance of complex networks. Nature 406:378–382
Amaral L, Scala A, Barthélémy M, Stanley H (2002) Classes of small-world networks. Proc Natl Acad Sci 97(21):11149–11152
Bagler G (2008) Analysis of the airport network of india as a complex weighted network. Phys A 387(12):2972–2980
Bagrow J, Wang D, Barábasi A (2011) Collective response of human populations to large-scale emergencies. PLoS One 6:e17680
Barabási A, Albert R, Jeong H (2000) Scale-free characteristics of random networks: the topology of the world-wide web. Phys A 281:69–77
Barrat A, Barthélémy M, Vespignani A (2005) The effects of spatial constraints on the evolution of weighted complex networks. J Stat Mech 2005:P05003
Blondel V, Guillaume J, Lambiotte R, Lefebvre R (2008) Fast unfolding of community hierarchies in large networks. J Stat Mech 2008:P10008
Buldyrev S, Parshani R, Paul G, Stanley H, Havlin S (2010) Catastrophic cascade of failures in interdependent networks. Nature 464:1025–1028
Bye B (2011) Volcanic eruptions: science and risk management. http://www.science20.com/planetbye/volcanic_eruptions_science_and_risk_management-79456. Accessed 11 Aug 2015
Callaway D, Newman M, Strogatz S, Watts D (2000) Network robustness and fragility: Percolation on random graphs. Phys Rev Lett 85:5468
Chassin D, Posse C (2005) Evaluating north american electric grid reliability using the barabási-albert network model. Phys A 355(2):667–677
Colizza V, Barrat A, Barthélémy M, Vespignani A (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci 103:2015–2020
Colizza V, Barrat A, Barthélémy M, Valleron A, Vespignani A (2007) Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Med 4:e13
Danon L, Duch J, Díaz-Guilera A, Arenas A (2005) Comparing community structure identification. J Stat Mech 09:P09008
Epstein J, Goedecke D, Yu F, Morris R, Wagener D et al (2007) Controlling pandemic: the value of international air travel restrictions. PLoS One 2:e401
Erdös P, Rényi A (1959) On random graphs. Publ Math 6:290–297
Freeman L (1977) A set of measures of centrality based upon betweenness. Sociometry 40:35–41
Guimera R, Amaral L (2004a) Functional cartography of complex metabolic networks. Nature 433(7028):895–900
Guimera R, Amaral L (2004b) Modeling the world-wide airport network. Eur Phys J B 38:381–385
Guimera R, Mossa S, Turtschi A, Amaral L (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc Natl Acad Sci 102(22):7794–7799
Holme P, Kim B, Yoon C, Han S (2002) Attack vulnerability of complex networks. Phys Rev E 65:056109
Hufnagel L, Brockmann D, Geisel T (2004) Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci 101:15124–15129
Kinney R, Crucitti P, Albert R, Latora V (2005) Modeling cascading failures in the north american power grid. Eur Phys J B 46:101–107
Krapivsky P, Redner S (2002) A statistical physics perspective on web growth. Comput Netw 39:261–276
Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701–198704
May R, Levin S, Sugihara G (2008) Complex systems: ecology for bankers. Nature 451:893–895
Meza O, Grady D, Thiemann D, Bagrow J, Brockmann D (2013) Eyjafjallajökull and 9/11: the impact of large-scale disasters on worldwide mobility. PloS One 8:e69829
Motter A, Lai Y (2002) Cascade-based attacks on complex networks. Phys Rev E 66:065102(R)
Newman M (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133
OpenFlights (2014) http://openflights.org
Tran Q, Namatame A (2014) Design robust networks against overload-based cascading failures. Int J Comput Sci Artif Intell 4(2):35–44
Verma T, Araújo N, Herrmann H (2014) Revealing the structure of the world airline network. Sci Rep 4:5638
Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325:425–428
Wang J, Mo H, Wang F, Jin F (2011) Exploring the network structure and nodal centrality of china’s air transport network: a complex network approach. J Transp Geogr 19(4):712–721
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Tran, Q.H.A., Namatame, A. Worldwide aviation network vulnerability analysis: a complex network approach. Evolut Inst Econ Rev 12, 349–373 (2015). https://doi.org/10.1007/s40844-015-0025-y
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DOI: https://doi.org/10.1007/s40844-015-0025-y