Evaluating balancing on social networks through the efficient solution of Correlation Clustering problems - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EURO Journal on Computational Optimization Année : 2017

Evaluating balancing on social networks through the efficient solution of Correlation Clustering problems

Résumé

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.
Fichier principal
Vignette du fichier
main.pdf (491.91 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02178542 , version 1 (10-07-2019)

Identifiants

Citer

Mario Levorato, Rosa Figueiredo, Yuri Y. Frota, Lúcia Drummond. Evaluating balancing on social networks through the efficient solution of Correlation Clustering problems. EURO Journal on Computational Optimization, 2017, 5 (4), pp.467-498. ⟨10.1007/s13675-017-0082-6⟩. ⟨hal-02178542⟩

Collections

UNIV-AVIGNON LIA
61 Consultations
249 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More