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Evaluating balancing on social networks through the efficient solution of correlation clustering problems

Author

Listed:
  • Mario Levorato

    (Fluminense Federal University)

  • Rosa Figueiredo

    (University of Avignon)

  • Yuri Frota

    (Fluminense Federal University)

  • Lúcia Drummond

    (Fluminense Federal University)

Abstract

One challenge for social network researchers is to evaluate balance in a social network. The degree of balance in a social group can be used as a tool to study whether and how this group evolves to a possible balanced state. The solution of clustering problems defined on signed graphs can be used as a criterion to measure the degree of balance in social networks and this measure can be obtained with the optimal solution of the correlation clustering problem, as well as a variation of it, the relaxed correlation clustering problem. However, solving these problems is no easy task, especially when large network instances need to be analyzed. In this work, we contribute to the efficient solution of both problems by developing sequential and parallel ILS metaheuristics. Then, by using our algorithms, we solve the problem of measuring the structural balance on large real-world social networks.

Suggested Citation

  • Mario Levorato & Rosa Figueiredo & Yuri Frota & Lúcia Drummond, 2017. "Evaluating balancing on social networks through the efficient solution of correlation clustering problems," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 467-498, December.
    • Handle: RePEc:spr:eurjco:v:5:y:2017:i:4:d:10.1007_s13675-017-0082-6
    DOI: 10.1007/s13675-017-0082-6
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    References listed on IDEAS

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    Cited by:

    1. Dewan F. Wahid & Elkafi Hassini, 2022. "A Literature Review on Correlation Clustering: Cross-disciplinary Taxonomy with Bibliometric Analysis," SN Operations Research Forum, Springer, vol. 3(3), pages 1-42, September.
    2. Eduardo Queiroga & Anand Subramanian & Rosa Figueiredo & Yuri Frota, 2021. "Integer programming formulations and efficient local search for relaxed correlation clustering," Journal of Global Optimization, Springer, vol. 81(4), pages 919-966, December.

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