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Network Analysis of US Air Transportation Network

  • Guangying Hua
  • Yingjie Sun
  • Dominique Haughton
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 12)

Abstract

There has been a considerable growth in interest in network analysis. Air transportation networks are regarded as complex networks which are full of dynamics and complexity. This study focuses on the US air transportation network, which is one of the most diverse and dynamic transportation networks in the world. All of the data are drawn from the US Bureau of Transportation Statistics (BTS). The topology features show that the network is a scale-free small-world network; the degree distribution follows a truncated power law. The network also confirms the 9/11 impact on the US air travel industry. A discrete dynamic model is constructed to investigate the evolution of the network. Our analysis offers direct confirmation for the existence of preferential attachment in the air transportation network. We conclude that both an aging effect and preferential attachment are the two mechanisms driving the network evolution.

Keywords

Degree Distribution Cluster Coefficient Preferential Attachment Aviation Industry Circle Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Dorogovtsev, S.N. and Mendes, J.F.F. Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford: Oxford University Press, 2003.Google Scholar
  2. 2.
    Barabasi, A.L. and Albert, R. Emergence of scaling in random networks. Science, 286:509–512, 1999.CrossRefGoogle Scholar
  3. 3.
    Borgatti, S.P., Mehra, A., Brass, D.J., and Labianca, G. Network analysis in the social sciences. Science, 323:892–895, 2009.Google Scholar
  4. 4.
    Watts, D.J. and Strogatz, S.H. Collective dynamics of ‘small-world’ networks. Nature, 393:409–410, 1998.Google Scholar
  5. 5.
    Guimera, R. et al. The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles. In Proceedings of the National Academy of Sciences, 102:7794–7799, 2005.CrossRefGoogle Scholar
  6. 6.
    Guimerá, R. and Amaral, L. Modeling the world-wide airport network. The European Physical Journal B – Condensed Matter, 38:381–385, 2004.CrossRefGoogle Scholar
  7. 7.
    Li, W. and Cai, X. Statistical analysis of airport network of China. Physical Review, 69:046106, 2004.Google Scholar
  8. 8.
    Bagler, G. Analysis of the airport network of India as a complex weighted network. Physical A: Statistical Mechanics and its Application s, 387:2972, 2008.CrossRefGoogle Scholar
  9. 9.
    Brandes, U. and Erlebach, T. Network Analysis: Methodological Foundations. New York, NY: Springer, 2005.  Google Scholar
  10. 10.
    Hanneman, R.A. and Riddle, M.  Introduction to Social Network Methods.  Riverside, CA:  University of California, 2005.Google Scholar
  11. 11.
    Bird, C., Gourley, A., and Swaminathan, A. Mining email social networks, MSR'06, Shanghai, China, 2006.Google Scholar
  12. 12.
    Mislove, A., Koppula,H.S., Gummadi, K.P., Druschel, P., and Bhattacharjee, B. Growth of the flickr social network, ACM SIGCOMM Workshop on Online Social Networks, Seattle, WA, USA, 2008.Google Scholar
  13. 13.
    Gay, B. and Dousset, B. Innovation and network structural dynamics: Study of the alliance network of a major sector of the biotechnology industry. Research Policy, 34:1457–1475, Dec. 2005.CrossRefGoogle Scholar
  14. 14.
    Eisenberg, E. and Levanon, E.Y. Preferential attachment in the protein network evolution. Physical Review Letters, 91:138701, 2003.CrossRefGoogle Scholar
  15. 15.
    Boginski, V., Butenko, S., and Pardalos, P.M. Statistical analysis of financial networks. Computational Statistics & Data Analysis , 48:431–443, 2005.CrossRefGoogle Scholar
  16. 16.
    Huber, H. Inside the mechanics of network development: How competition and strategy reorganize Europe air traffic. Journal of Air Transportation, 11:64–86, 2006.  Google Scholar
  17. 17.
    Wasserman, S. and Faust, K. Social Network Analysis: Methods and Application. Cambridge: Cambridge University Press, 1994.Google Scholar
  18. 18.
    Bhadra, D. and Texter, P. Airline networks: An econometric framework to analyze domestic U.S. air travel. Journal of Transportation and Statistics, 7:87–102, 2004.Google Scholar
  19. 19.
    de Nooy, W., Mrvar, A., and Batagelj, V. Exploratory Social Network Analysis with Pajek. Cambridge: Cambridge University Press, 2005.Google Scholar
  20. 20.
    Jeong, H., Néda, Z., and Barabási, A.L. Measuring preferential attachment in evolving networks. Europhysics Letters, 61:567–572, 2003.CrossRefGoogle Scholar
  21. 21.
    Krapivsky, P.L., Redner, S., and Leyvraz, F. Connectivity of growing random networks. Physical Review Letters, 85:4629, 2000.CrossRefGoogle Scholar
  22. 22.
    Amaral, L.A.N. et al. Classes of small-world networks, In Proceedings of the National Academy of Sciences of the United States of America, 97:11149–11152, Oct. 2000.CrossRefGoogle Scholar

Copyright information

© Springer US 2010

Authors and Affiliations

  • Guangying Hua
    • 1
  • Yingjie Sun
    • 2
  • Dominique Haughton
    • 1
  1. 1.Department of Mathematical SciencesBentley UniversityWalthamUSA
  2. 2.Department of Biomedical engineeringBoston UniversityBostonUSA

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