Efficient Solution of the Correlation Clustering Problem: An Application to Structural Balance

Lúcia Drummond², Rosa Figueiredo¹, Yuri Frota², and Mário Levorato³

¹CIDMA, Department of Mathematics, University of Aveiro 3810-193, Aveiro, Portugal
rosa.figueiredo@ua.pt

²Department of Computer Science, Fluminense Federal University 24210-240, Niterói, RJ, Brazil
lucia@ic.uff.br
yuri@ic.uff.br

³Petróleo Brasileiro S.A., Brazil
levorato@petrobras.com.br

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. By considering the original definition of the structural balance, the optimal solution of the Correlation Clustering (CC) Problem arises as one possible measure. In this work, we contribute to the efficient solution of the CC problem by developing sequential and parallel GRASP metaheuristics. Then, by using our GRASP algorithms, we solve the problem of measuring the structural balance of large social networks.

LNCS 8186, p. 674 ff.

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