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Robustness Elasticity in Complex Networks

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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