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Robustness in network community detection under links weights uncertainties

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  • Ramirez-Marquez, J.E.
  • Rocco, C.M.
  • Moronta, J.
  • Gama Dessavre, D.

Abstract

In network analysis, a community can be defined as a group of nodes of a network (or clusters) that are densely interconnected with each other but only sparsely connected with the rest of the network. Several algorithms have been used to obtain a convenient partition allowing extracting the communities in a given network, based on their topology and, possibly, the weights of links. These weights usually represent specific characteristics for example: distance, reactance, reliability. Even if the optimum partitions could be derived, there are uncertainties associated to the network parameters that affect the network partition. In this paper, the authors extend a previous approach for assessing the effects of weight uncertainties on community structures and propose a global approach for (a) understanding the global similarity among the partitions; (b) analyzing the robustness of the communities derived without uncertainty; and (c) quantifying the robustness of the inter-community links. To this aim an uncertainty propagation analysis, based on the Monte Carlo technique is proposed. The approach is illustrated through analyzing the topology of an electric power system.

Suggested Citation

  • Ramirez-Marquez, J.E. & Rocco, C.M. & Moronta, J. & Gama Dessavre, D., 2016. "Robustness in network community detection under links weights uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 88-95.
    • Handle: RePEc:eee:reensy:v:153:y:2016:i:c:p:88-95
    DOI: 10.1016/j.ress.2016.04.009
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    References listed on IDEAS

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    1. Luca Marotta & Salvatore Miccichè & Yoshi Fujiwara & Hiroshi Iyetomi & Hideaki Aoyama & Mauro Gallegati & Rosario N Mantegna, 2015. "Bank-Firm Credit Network in Japan: An Analysis of a Bipartite Network," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. 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.
    4. Fan, Ying & Li, Menghui & Zhang, Peng & Wu, Jinshan & Di, Zengru, 2007. "The effect of weight on community structure of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 583-590.
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    Cited by:

    1. Bilal, Saoud & Abdelouahab, Moussaoui, 2017. "Evolutionary algorithm and modularity for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 89-96.
    2. 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.
    3. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Rocco, Claudio M. & Moronta, José & Ramirez-Marquez, José E. & Barker, Kash, 2017. "Effects of multi-state links in network community detection," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 46-56.

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