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Quantifying the resilience of community structures in networks

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  • Ramirez-Marquez, Jose E.
  • Rocco, Claudio M.
  • Barker, Kash
  • Moronta, Jose

Abstract

Many networks contain community structures, or collections of densely connected nodes with sparse connections to other dense groups in the network. Communities may coalesce for a number of reasons, including friendships in a social network, physical connections in an infrastructure network, or spatial distribution in a neighborhood. Several approaches have been proposed to identify communities and compare the partition of networks into communities. This work explores community structures from the perspective of their resilience, or their ability to withstand degradation in network performance and recover to a desired level of network performance. In this context, network performance is defined as the similarity of a network partition (or the characterization of the network into community structures) formed after the disconnection of one or more links to the initial partition. This work provides an approach to measure how the initial set of community structures survive after a disruption and how these structures return after restoration commences. The approach is illustrated with an electric power network case study.

Suggested Citation

  • Ramirez-Marquez, Jose E. & Rocco, Claudio M. & Barker, Kash & Moronta, Jose, 2018. "Quantifying the resilience of community structures in networks," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 466-474.
  • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:466-474
    DOI: 10.1016/j.ress.2017.09.019
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    References listed on IDEAS

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    3. Mishra, Dillip Kumar & Ghadi, Mojtaba Jabbari & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2021. "A review on resilience studies in active distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
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    6. Amanda Melendez & David Caballero-Russi & Mariantonieta Gutierrez Soto & Luis Felipe Giraldo, 2022. "Computational models of community resilience," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1121-1152, March.
    7. Cerqueti, Roy & Ferraro, Giovanna & Iovanella, Antonio, 2019. "Measuring network resilience through connection patterns," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 320-329.
    8. Cerqueti, Roy & Ciciretti, Rocco & Dalò, Ambrogio & Nicolosi, Marco, 2022. "A new measure of the resilience for networks of funds with applications to socially responsible investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    9. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    10. Maddah, Negin & Heydari, Babak, 2024. "Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    11. Xu, Zizhen & Chopra, Shauhrat S., 2022. "Network-based Assessment of Metro Infrastructure with a Spatial–temporal Resilience Cycle Framework," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    12. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    13. Almoghathawi, Yasser & Selim, Shokri & Barker, Kash, 2023. "Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    14. Caputo, Antonio C. & Kalemi, Bledar & Paolacci, Fabrizio & Corritore, Daniele, 2020. "Computing resilience of process plants under Na-Tech events: Methodology and application to sesmic loading scenarios," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    15. Lee, Joohyun & Kwak, Jaewook & Lee, Hyang-Won & Shroff, Ness B., 2018. "Finding minimum node separators: A Markov chain Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 225-235.
    16. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    17. Elkady, Sahar & Hernantes, Josune & Labaka, Leire, 2023. "Towards a resilient community: A decision support framework for prioritizing stakeholders' interaction areas," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    18. Nogal, M. & Honfi, D., 2019. "Assessment of road traffic resilience assuming stochastic user behaviour," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 72-83.

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