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

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...

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Autores principales:	Mario Levorato, Rosa Figueiredo, Yuri Frota, Lúcia Drummond
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2017
Materias:	90C35 05C22 91D30 90C59 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/731f7de9829440968feee5f1a3ba8647
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spelling	oai:doaj.org-article:731f7de9829440968feee5f1a3ba86472021-12-02T05:01:03ZEvaluating balancing on social networks through the efficient solution of correlation clustering problems2192-440610.1007/s13675-017-0082-6https://doaj.org/article/731f7de9829440968feee5f1a3ba86472017-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621000897https://doaj.org/toc/2192-4406One 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.Mario LevoratoRosa FigueiredoYuri FrotaLúcia DrummondElsevierarticle90C3505C2291D3090C59Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 5, Iss 4, Pp 467-498 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	90C35 05C22 91D30 90C59 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90C35 05C22 91D30 90C59 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Mario Levorato Rosa Figueiredo Yuri Frota Lúcia Drummond Evaluating balancing on social networks through the efficient solution of correlation clustering problems
description	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.
format	article
author	Mario Levorato Rosa Figueiredo Yuri Frota Lúcia Drummond
author_facet	Mario Levorato Rosa Figueiredo Yuri Frota Lúcia Drummond
author_sort	Mario Levorato
title	Evaluating balancing on social networks through the efficient solution of correlation clustering problems
title_short	Evaluating balancing on social networks through the efficient solution of correlation clustering problems
title_full	Evaluating balancing on social networks through the efficient solution of correlation clustering problems
title_fullStr	Evaluating balancing on social networks through the efficient solution of correlation clustering problems
title_full_unstemmed	Evaluating balancing on social networks through the efficient solution of correlation clustering problems
title_sort	evaluating balancing on social networks through the efficient solution of correlation clustering problems
publisher	Elsevier
publishDate	2017
url	https://doaj.org/article/731f7de9829440968feee5f1a3ba8647
work_keys_str_mv	AT mariolevorato evaluatingbalancingonsocialnetworksthroughtheefficientsolutionofcorrelationclusteringproblems AT rosafigueiredo evaluatingbalancingonsocialnetworksthroughtheefficientsolutionofcorrelationclusteringproblems AT yurifrota evaluatingbalancingonsocialnetworksthroughtheefficientsolutionofcorrelationclusteringproblems AT luciadrummond evaluatingbalancingonsocialnetworksthroughtheefficientsolutionofcorrelationclusteringproblems
_version_	1718400836108812288

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

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