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Topological Structure of US Flight Network Based on Complex Network Theory 被引量:1

Topological Structure of US Flight Network Based on Complex Network Theory
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摘要 US flight network,composed of 285airports(nodes)and 3 971flights(edges)is studied.A static network model and a dynamic network model of US flight network are established.Firstly,the characteristics of static network are analyzed.One finds that such a network is a″small-world″and″scale-free″network.The cumulative degree distributions of weighted network and unweighted network follow″Double Pareto Law″.And the degree exponent of weighted network is smaller than unweighted network.The average shortest-path length is 2.368 9,which is smaller than previous results.The clustering coefficient of unweighted network is 0.637 1and of weighted network is 0.653 6,which are both bigger than previous results.The correlation of degree,unweighted clustering coefficient and weighted clustering coefficient are also discussed.Secondly,the characteristics of dynamic network are studied.The structure of flight network is changing as the time goes by on a day.In rush hours,the network′s character of″scale-free″is stronger than other times.And then the relationships of topological structures and congestion effects are addressed. US flight network,composed of 285airports(nodes)and 3 971flights(edges)is studied.A static network model and a dynamic network model of US flight network are established.Firstly,the characteristics of static network are analyzed.One finds that such a network is a″small-world″and″scale-free″network.The cumulative degree distributions of weighted network and unweighted network follow″Double Pareto Law″.And the degree exponent of weighted network is smaller than unweighted network.The average shortest-path length is 2.368 9,which is smaller than previous results.The clustering coefficient of unweighted network is 0.637 1and of weighted network is 0.653 6,which are both bigger than previous results.The correlation of degree,unweighted clustering coefficient and weighted clustering coefficient are also discussed.Secondly,the characteristics of dynamic network are studied.The structure of flight network is changing as the time goes by on a day.In rush hours,the network′s character of″scale-free″is stronger than other times.And then the relationships of topological structures and congestion effects are addressed.
出处 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2015年第5期555-559,共5页 南京航空航天大学学报(英文版)
基金 supported by the Projects in the National Science & Technology Pillar Program (2011BAH24B10) the Joint Funds of National Natural Science Foundation of China (61571441) the Fundamental Research Funds for the Central Universities of Civil Aviation University of China in 2016 the Open Fund of Air Traffic Management Research Base(No.KGJD201503) the Scientific Research Foundation of Civil Aviation University of China(No.2014QD01S)
关键词 complex network SCALE-FREE SMALL-WORLD congestion effect complex network scale-free small-world congestion effect
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  • 1Watts D J, Strogatz S H. Collective dynamics of "small-world" networks [J]. Nature, 1998, 393 (6684): 440-442.
  • 2Barabdsi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286 (5439): 509-512.
  • 3Amaral L A N, Scala A, Barthelemy M, et al. Clas- ses of small-world networks[J]. Proceedings of the National Academy of Sciences, 2000, 97 (21) : 11149-11152.
  • 4Guimer'a R, Mossa S, Turtschi A, et al. The world-wide air transportation network: Anomalous central- ity, community structure, and cities global roles[J]. Proceedings of the National Academy of Sciences, 2005, 102(22): 7794-7799.
  • 5Li W, Cai X. Statistical analysis of airport network of China[J]. Physical Review E, 2004, 69 (4): 046106.
  • 6Barrat A, Barthelemy M, Pastor-Satorras R, et al. The architecture of complex weighted networks[J]. Proceedings of the National Academy of Sciences, 2004, 101(11): 3747-3752.
  • 7Guimera R, Amaral L A N. Modeling the world- wide airport network[J]. The European Physical Journal B-Condensed Matter and Complex Systems, 2004, 38(2) : 381-385.
  • 8Colizza V, Barrat A, Barthclemy M, et al. The role of the airline transportation network in the prediction and predictability of global epidemics[J]. Proceed- ings of the National Academy of Sciences, 2006, 103 (7) : 2015-2020.
  • 9Gautreau A, Barrat A, Barthalemy M. Microdynam- ics in stationary complex networks[J]. Proceedings of the N#tional Academy of Sciences, 2009, 106 (22) : 8847-8852.
  • 10Zhang J, Cao X B, Du W B, et al. Evolution of Chi- nese airport network[J]. Physica A: Statistical Me- chanics and Its Applications, 2010, 389 (18): 3922- 3931.

二级参考文献16

  • 1Erdoes P and Rényi 1959 Publ. Math. (Debrecen) 6 290.
  • 2Watts D J and Strogatz S H 1998 Nature 393 440.
  • 3Barabási A-L and Albert R 1999 Science 286 509.
  • 4Albert R and Barabási A-L 2002 Rev. Mod. Phys. 74.
  • 5Kochen M 1989 The Small World (Norwood: Albex).
  • 6Barabási A-L, Albert R and Jeong H 2000 Physica A 281 9.
  • 7Bollobás B 1981 Siscrete Math. 33 1.
  • 8Pareto V 1897 Cours d'Economie Politique 2 (Lausanne:Université de Lausanne).
  • 9Adamic L A 2000 Preprint http://www.hpl.hp.com/shl/paoers/ranking.
  • 10Li W and Cai X 2000 Phys. Rev. E 61 771.

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