IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0039788.html
   My bibliography  Save this article

Robustness Elasticity in Complex Networks

Author

Listed:
  • Timothy C Matisziw
  • Tony H Grubesic
  • Junyu Guo

Abstract

Network robustness refers to a network’s resilience to stress or damage. Given that most networks are inherently dynamic, with changing topology, loads, and operational states, their robustness is also likely subject to change. However, in most analyses of network structure, it is assumed that interaction among nodes has no effect on robustness. To investigate the hypothesis that network robustness is not sensitive or elastic to the level of interaction (or flow) among network nodes, this paper explores the impacts of network disruption, namely arc deletion, over a temporal sequence of observed nodal interactions for a large Internet backbone system. In particular, a mathematical programming approach is used to identify exact bounds on robustness to arc deletion for each epoch of nodal interaction. Elasticity of the identified bounds relative to the magnitude of arc deletion is assessed. Results indicate that system robustness can be highly elastic to spatial and temporal variations in nodal interactions within complex systems. Further, the presence of this elasticity provides evidence that a failure to account for nodal interaction can confound characterizations of complex networked systems.

Suggested Citation

  • Timothy C Matisziw & Tony H Grubesic & Junyu Guo, 2012. "Robustness Elasticity in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
  • Handle: RePEc:plo:pone00:0039788
    DOI: 10.1371/journal.pone.0039788
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039788
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0039788&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0039788?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
    ---><---

    References listed on IDEAS

    as
    1. Christian Behrends & Mathew E. Sowa & Steven P. Gygi & J. Wade Harper, 2010. "Network organization of the human autophagy system," Nature, Nature, vol. 466(7302), pages 68-76, July.
    2. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodríguez-Núñez, Eduardo & García-Palomares, Juan Carlos, 2014. "Measuring the vulnerability of public transport networks," Journal of Transport Geography, Elsevier, vol. 35(C), pages 50-63.
    2. Nima Haghighi & S. Kiavash Fayyaz & Xiaoyue Cathy Liu & Tony H. Grubesic & Ran Wei, 2018. "A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment," Networks and Spatial Economics, Springer, vol. 18(1), pages 181-203, March.
    3. López, Fernando A. & Páez, Antonio & Carrasco, Juan A. & Ruminot, Natalia A., 2017. "Vulnerability of nodes under controlled network topology and flow autocorrelation conditions," Journal of Transport Geography, Elsevier, vol. 59(C), pages 77-87.
    4. Liu, Xiaolei & Lei, Zengxiang & Duan, Zhengyu, 2024. "Assessing metro network vulnerability with turn-back operations: A Monte Carlo method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    5. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong, 2013. "The impact of connection density on scale-free distribution in random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2547-2554.
    6. Xing Zhou & Wei Peng & Zhen Xu & Bo Yang, 2015. "Hardness Analysis and Empirical Studies of the Relations among Robustness, Topology and Flow in Dynamic Networks," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-29, December.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Yao, Jialing & Sun, Bingbin & Xi, lifeng, 2019. "Fractality of evolving self-similar networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 211-216.
    9. Sanjeev Goyal & Adrien Vigier, 2014. "Attack, Defence, and Contagion in Networks," Review of Economic Studies, Oxford University Press, vol. 81(4), pages 1518-1542.
    10. Britta Hoyer & Kris De Jaegher, 2023. "Network disruption and the common-enemy effect," International Journal of Game Theory, Springer;Game Theory Society, vol. 52(1), pages 117-155, March.
    11. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    12. Lordan, Oriol & Sallan, Jose M., 2019. "Core and critical cities of global region airport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 724-733.
    13. Diana Tampu & Carmen Costea, 2013. "Why society is a complex problem? A review of Philip Ball's book: Meeting Twentyfirst Century Challenges with a New Kind of Science," Journal of Economic Development, Environment and People, Alliance of Central-Eastern European Universities, vol. 2(1), pages 80-89, March.
    14. Elosegui, Pedro & Forte, Federico D. & Montes-Rojas, Gabriel, 2022. "Network structure and fragmentation of the Argentinean interbank markets," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    15. Liu, Run-Ran & Chu, Changchang & Meng, Fanyuan, 2023. "Higher-order interdependent percolation on hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    16. Li, Yapeng & Qiao, Shun & Deng, Ye & Wu, Jun, 2019. "Stackelberg game in critical infrastructures from a network science perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 705-714.
    17. Ohsung Kwon & Sung-guan Yun & Seung Hun Han & Yang Hon Chung & Duk Hee Lee, 2018. "Network Topology and Systemically Important Firms in the Interfirm Credit Network," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 847-864, April.
    18. Ho-Chun Herbert Chang & Brooke Harrington & Feng Fu & Daniel Rockmore, 2023. "Complex Systems of Secrecy: The Offshore Networks of Oligarchs," Papers 2303.03371, arXiv.org.
    19. Yilun Shang, 2019. "Super Connectivity of Erd?s-Rényi Graphs," Mathematics, MDPI, vol. 7(3), pages 1-5, March.
    20. Accominotti, Olivier & Lucena-Piquero, Delio & Ugolini, Stefano, 2023. "Intermediaries’ substitutability and financial network resilience: A hyperstructure approach," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0039788. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.