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Network Topology Reconfiguration Based on Risk Management

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Abstract

A great deal of information and traffic circulate over the complex and largescale networks. Such examples abound in an airline route or road map, and the Internet web[1]. These are important social infrastructures and hence desired to possess sufficient resilience against an unpredictable breakdown in order to maintain the expected function. Including redundant nodes and links to the network is expected to allow more robust systems, however it would make the system more expensive and less efficient in the ordinary situations. Therefore, a well-balanced network topology taking into account both optimality and resilience is of great importance[2]. Recently, it has come to be known that a number of real world networks are scale-free network (SFN), as seen in the airline route map, electrical power network, and the Internet web[3, 4, 5]. One of the main features in SFN is that the network topology exhibits a power-law degree distribution: P(k) ~k  − γ, where k is the number of link attached to a randomly chosen node in the network and γ is the scaling exponent. SFN is named for the fact that the power-law distributions do not have a median thad implies a typical size of the system[6]. It is also known that the average path length between the nodes are surprisingly small in SFN[7], hence the efficient transport is expected over the network.

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References

  1. Echenique, P., gomez Gardenes, J., Moreno, Y.: Dynamics of jamming transitions in complex networks. cond-mat/0412053 (2004)

    Google Scholar 

  2. Valente, A.X.C.N., Sarkar, A., Stone, H.A.: Two-peak and three-peak optimal complex networks. Physical Review Letters 92(2), 118702 (2004)

    Article  Google Scholar 

  3. Huberman, B.A., Adamic, L.A.: Growth dynamics of the world wide web. Nature 401, 130 (1999)

    Article  Google Scholar 

  4. Faloutsos, C.F.M., Faloutsos, P.: On power-law relationships of the internet topology. ACM SIGCOMM 1999, Review 29, 251–263 (1999)

    Article  Google Scholar 

  5. Strogtz, S.H.: Exploring complex networks. Nature 410, 268–276 (2001)

    Article  Google Scholar 

  6. Barabasi, A.L.: The New Science of Networks. Perseus Books Group (2002)

    Google Scholar 

  7. Cohen, R., Havlin, S.: Scale-free networks are ultrasmall. Physical Review Letters 90(5), 058701 (2003)

    Article  Google Scholar 

  8. Albert, R., Jeong, H., Barabasi, A.-L.: Error and attack tolerance of complex networks. Nature 406, 378–382 (2000)

    Article  Google Scholar 

  9. Cohen, R., Erez, K., ben Avraham, D., Havlin, S.: Resilience of the internet to random breakdowns. Physical Review Letters 85(21), 4626–4628 (2000)

    Article  Google Scholar 

  10. Cohen, R., Erez, K., ben Avraham, D., Havlin, S.: Breakdown of the internet under intentional attack. Physical Review Letters 86(16), 3682–3685 (2001)

    Article  Google Scholar 

  11. Zhao, L., Park, K., Lai, Y.-C.: Attack vulnerability of scale-free networks due to cascading breakdown. Physical Review E 70(035101) (2004)

    Google Scholar 

  12. GoLmez-Gardenes, J., Moreno, Y.: Local versus global knowledge in the barabalsi-albert scale-free network model. Physical Review E 69(037103) (2004)

    Google Scholar 

  13. Kalisky, T., Sreenivasan, S., Braunstein, L.A., Buldyrev, S.V., Havlin, S., Stanley, H.E.: Scale-free networks emerging from weighted random graphs. cond-mat/0503598 (2005)

    Google Scholar 

  14. Motter1, A.E., Lai, Y.-C.: Cascade-based attacks on complex networks. Physical Review E 66(065102) (2002)

    Google Scholar 

  15. Crucitti, P., Latora, V., Marchiori, M., Rapisarda, A.: Error and attack tolerance of complex networks. Physica A 340, 388–394 (2004)

    Article  MathSciNet  Google Scholar 

  16. Guillaume, J.-L., Latapy, M., Magnien, C.: Comparison of failures and attacks on random and scale-free networks. In: Higashino, T. (ed.) OPODIS 2004. LNCS, vol. 3544, pp. 186–196. Springer, Heidelberg (2005)

    Google Scholar 

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Sekiyama, K., Araki, H. (2009). Network Topology Reconfiguration Based on Risk Management. In: Asama, H., Kurokawa, H., Ota, J., Sekiyama, K. (eds) Distributed Autonomous Robotic Systems 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00644-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-00644-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00643-2

  • Online ISBN: 978-3-642-00644-9

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