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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 (CC) problem, as well as a variation of it, the Relaxed Correlation Clustering (RCC) 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.

Evaluating balance in a social network has been a challenge for social network researchers. 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. In particular, the solution of the Correlation Clustering (CC) problem can be used as a criterion to measure the amount of balance in signed social networks, where positive (friendly) and negative (antagonistic) interactions take place. In this work, we provide an efficient solution of the CC problem by the use of the ILS metaheuristic. The proposed algorithm outperforms other solution strategies from literature in execution time, with the same solution quality.

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