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Community detection and resilience in multi-source, multi-terminal networks

Author

Listed:
  • Claudio M Rocco
  • Kash Barker
  • Jose Moronta
  • Jose E Ramirez-Marquez

Abstract

Many networks, particularly infrastructure networks, have multiple source nodes and multiple terminal nodes. And many such networks exhibit community structures, wherein the network is partitioned into groups of densely connected nodes with sparse connections between groups, based on topology or spatial characteristics, among others. This article proposes an approach for evaluating the effects of disruptive events, or the disconnection of network components due to failures or attacks, to the community structures and to the total network. The approach enables the assessment of resilience, evaluating both the vulnerability of the network and the recoverability enabled by different network restoration sequences. Different predefined restoration sequences are compared from different perspectives, including cost and strategy characteristics as well as resilience objectives (partial or complete restoration). The approach is illustrated with the topology of an electric power network.

Suggested Citation

  • Claudio M Rocco & Kash Barker & Jose Moronta & Jose E Ramirez-Marquez, 2018. "Community detection and resilience in multi-source, multi-terminal networks," Journal of Risk and Reliability, , vol. 232(6), pages 616-626, December.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:6:p:616-626
    DOI: 10.1177/1748006X17751516
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    References listed on IDEAS

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    1. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    2. Jonas Johansson & Henrik Hassel & Alexander Cedergren, 2011. "Vulnerability analysis of interdependent critical infrastructures: case study of the Swedish railway system," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 7(4), pages 289-316.
    3. Nicholson, Charles D. & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 62-73.
    4. Rocco S., Claudio M. & Emmanuel Ramirez-Marquez, José & Salazar A., Daniel E., 2010. "Bi and tri-objective optimization in the deterministic network interdiction problem," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 887-896.
    5. Hiba Baroud & Jose E. Ramirez‐Marquez & Kash Barker & Claudio M. Rocco, 2014. "Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1317-1335, July.
    6. Henry, Devanandham & Emmanuel Ramirez-Marquez, Jose, 2012. "Generic metrics and quantitative approaches for system resilience as a function of time," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 114-122.
    7. Rocco S., Claudio M. & Ramirez-Marquez, José Emmanuel, 2011. "Vulnerability metrics and analysis for communities in complex networks," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1360-1366.
    8. Barker, Kash & Ramirez-Marquez, Jose Emmanuel & Rocco, Claudio M., 2013. "Resilience-based network component importance measures," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 89-97.
    9. Hiba Baroud & Kash Barker & Jose E. Ramirez‐Marquez & Claudio M. Rocco, 2015. "Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 642-662, April.
    10. Johansson, Jonas & Hassel, Henrik & Zio, Enrico, 2013. "Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 27-38.
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