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On community structure validation in real networks

Author

Listed:
  • Mirko Signorelli

    (Leiden University)

  • Luisa Cutillo

    (University of Leeds)

Abstract

Community structure is a commonly observed feature of real networks. The term refers to the presence in a network of groups of nodes (communities) that feature high internal connectivity, but are poorly connected between each other. Whereas the issue of community detection has been addressed in several works, the problem of validating a partition of nodes as a good community structure for a real network has received considerably less attention and remains an open issue. We propose a set of indices for community structure validation of network partitions that are based on an hypothesis testing procedure that assesses the distribution of links between and within communities. Using both simulations and real data, we illustrate how the proposed indices can be employed to compare the adequacy of different partitions of nodes as community structures in a given network, to assess whether two networks share the same or similar community structures, and to evaluate the performance of different network clustering algorithms.

Suggested Citation

  • Mirko Signorelli & Luisa Cutillo, 2022. "On community structure validation in real networks," Computational Statistics, Springer, vol. 37(3), pages 1165-1183, July.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01156-6
    DOI: 10.1007/s00180-021-01156-6
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    References listed on IDEAS

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    1. Briatte, Franã‡Ois, 2016. "Network patterns of legislative collaboration in twenty parliaments," Network Science, Cambridge University Press, vol. 4(2), pages 266-271, June.
    2. Mirko Signorelli & Ernst C. Wit, 2018. "A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 355-369, February.
    3. Carissimo, Annamaria & Cutillo, Luisa & Feis, Italia De, 2018. "Validation of community robustness," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 1-24.
    4. M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
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    Cited by:

    1. Wang, Shuliang & Dong, Qiqi, 2023. "A multi-source power grid's resilience enhancement strategy based on subnet division and power dispatch," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).

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