IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v380y2007icp673-683.html
   My bibliography  Save this article

Optimizing complex networks for resilience against cascading failure

Author

Listed:
  • Ash, J.
  • Newth, D.

Abstract

Our modern society has come to depend on large-scale infrastructure networks to deliver resources to our homes and businesses in an efficient manner. Over the past 10years there have been numerous examples where a local disturbance has lead to the global failure of systems. In this paper, we use an evolutionary algorithm to evolve complex networks that are resilient to such cascading failure. We then analyze these networks for topological regularities that explain the source of such resilience. The analysis reveals that clustering, modularity and long path lengths all play an important part in the design of robust large-scale infrastructure.

Suggested Citation

  • Ash, J. & Newth, D., 2007. "Optimizing complex networks for resilience against cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 673-683.
  • Handle: RePEc:eee:phsmap:v:380:y:2007:i:c:p:673-683
    DOI: 10.1016/j.physa.2006.12.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437107002543
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2006.12.058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sanjeev Goyal & Fernando Vega-Redondo, 2000. "Learning, Network Formation and Coordination," Econometric Society World Congress 2000 Contributed Papers 0113, Econometric Society.
    2. Quayle, A.P. & Siddiqui, A.S. & Jones, S.J.M., 2006. "Preferential network perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 823-840.
    3. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.
    4. Bálint Mészáros & István Simon & Zsuzsanna Dosztányi, 2009. "Prediction of Protein Binding Regions in Disordered Proteins," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    5. Irina Rish & Guillermo Cecchi & Benjamin Thyreau & Bertrand Thirion & Marion Plaze & Marie Laure Paillere-Martinot & Catherine Martelli & Jean-Luc Martinot & Jean-Baptiste Poline, 2013. "Schizophrenia as a Network Disease: Disruption of Emergent Brain Function in Patients with Auditory Hallucinations," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-15, January.
    6. Wang, Zhuoyang & Chen, Guo & Hill, David J. & Dong, Zhao Yang, 2016. "A power flow based model for the analysis of vulnerability in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 105-115.
    7. Bellingeri, Michele & Cassi, Davide & Vincenzi, Simone, 2014. "Efficiency of attack strategies on complex model and real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 174-180.
    8. Bech, Morten L. & Atalay, Enghin, 2010. "The topology of the federal funds market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5223-5246.
    9. Valentini, Luca & Perugini, Diego & Poli, Giampiero, 2007. "The “small-world” topology of rock fracture networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 323-328.
    10. Enrico Zio & Giovanni Sansavini, 2011. "Component Criticality in Failure Cascade Processes of Network Systems," Risk Analysis, John Wiley & Sons, vol. 31(8), pages 1196-1210, August.
    11. Ryan M. Hynes & Bernardo S. Buarque & Ronald B. Davies & Dieter F. Kogler, 2020. "Hops, Skip & a Jump - The Regional Uniqueness of Beer Styles," Working Papers 202013, Geary Institute, University College Dublin.
    12. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    13. Lenore Newman & Ann Dale, 2007. "Homophily and Agency: Creating Effective Sustainable Development Networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 9(1), pages 79-90, February.
    14. Aybike Ulusan & Ozlem Ergun, 2018. "Restoration of services in disrupted infrastructure systems: A network science approach," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-28, February.
    15. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    16. Deng, Ye & Wu, Jun & Tan, Yue-jin, 2016. "Optimal attack strategy of complex networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 74-81.
    17. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    18. Alexander Shiroky & Andrey Kalashnikov, 2021. "Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures," Mathematics, MDPI, vol. 9(19), pages 1-11, October.
    19. Anand, Kartik & Gai, Prasanna & Marsili, Matteo, 2012. "Rollover risk, network structure and systemic financial crises," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1088-1100.
    20. Zhao, Jiuhua & Liu, Qipeng & Wang, Lin & Wang, Xiaofan, 2017. "Competitive seeds-selection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 240-248.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:380:y:2007:i:c:p:673-683. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.