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

Mario Levorato (), Rosa Figueiredo (), Yuri Frota () and Lúcia Drummond ()
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Mario Levorato: Fluminense Federal University
Rosa Figueiredo: University of Avignon
Yuri Frota: Fluminense Federal University
Lúcia Drummond: Fluminense Federal University

EURO Journal on Computational Optimization, 2017, vol. 5, issue 4, No 2, 467-498

Abstract: 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; 90C35; 05C22; 91D30; 90C59 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13675-017-0082-6

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