Multiple partitioning of multiplex signed networks: Application to European parliament votes

Nejat Arinik (1) , Rosa Figueiredo (1) , Vincent Labatut (1)

1 LIA - Laboratoire Informatique d'Avignon

For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated to their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.

Mots clés: Graph Partitioning; Signed Graphs; European Parliament; Multiplex Graphs; Correlation Clustering; Structural Balance; Signed Graph; Multiplex Graph

Domaines: Recherche opérationnelle [math.OC]; Réseaux sociaux et d'information [cs.SI]

Nejat Arinik, Rosa Figueiredo, Vincent Labatut. Multiple partitioning of multiplex signed networks: Application to European parliament votes. Social Networks, 2020, 60, pp.83-102. ⟨10.1016/j.socnet.2019.02.001⟩. ⟨hal-02082574v2⟩