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Unsupervised spectral clustering for hierarchical modelling and criticality analysis of complex networks

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  • Fang, Yi-Ping
  • Zio, Enrico

Abstract

Infrastructure networks are essential to the socioeconomic development of any country. This article applies clustering analysis to extract the inherent structural properties of realistic-size infrastructure networks. Network components with high criticality are identified and a general hierarchical modelling framework is developed for representing the networked system into a scalable hierarchical structure of corresponding fictitious networks. This representation makes a multi-scale criticality analysis possible, beyond the widely used component-level criticality analysis, whose results obtained from zoom-in analysis can support confident decision making.

Suggested Citation

  • Fang, Yi-Ping & Zio, Enrico, 2013. "Unsupervised spectral clustering for hierarchical modelling and criticality analysis of complex networks," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 64-74.
    • Handle: RePEc:eee:reensy:v:116:y:2013:i:c:p:64-74
    DOI: 10.1016/j.ress.2013.02.021
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    References listed on IDEAS

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    1. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    2. 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.
    3. Zio, E. & Golea, L.R. & Rocco S., C.M., 2012. "Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 172-177.
    4. E. Zio, 2007. "From complexity science to reliability efficiency: a new way of looking at complex network systems and critical infrastructures," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 3(3/4), pages 488-508.
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    Cited by:

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    2. Floreale, Giovanni & Baraldi, Piero & Lu, Xuefei & Rossetti, Paolo & Zio, Enrico, 2024. "Sensitivity analysis by differential importance measure for unsupervised fault diagnostics," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    5. Rocchetta, Roberto, 2022. "Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    7. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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