EURO Journal on Computational Optimization

, Volume 5, Issue 4, pp 467–498 | Cite as

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

  • Mario Levorato
  • Rosa Figueiredo
  • Yuri Frota
  • Lúcia Drummond
Original Paper
First Online:
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Accepted:
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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.

Keywords

Correlation clustering Social network Structural balance ILS VND 

Mathematics Subject Classification

90C35 05C22 91D30 90C59 
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Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

Supplementary material

13675_2017_82_MOESM1_ESM.pdf (219 kb)
Supplementary material 1 (pdf 218 KB)

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Copyright information

© EURO - The Association of European Operational Research Societies 2017

Authors and Affiliations

  • Mario Levorato
    • 1
  • Rosa Figueiredo
    • 2
  • Yuri Frota
    • 1
  • Lúcia Drummond
    • 1
  1. 1.Department of Computer ScienceFluminense Federal UniversityNiteróiBrazil
  2. 2.Laboratoire d’Informatique d’AvignonUniversity of AvignonAvignonFrance

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