Multiple partitioning of multiplex signed networks Application to European parliament votes

被引：17

作者：

Arinik, Nejat ^{[1
]}

Figueiredo, Rosa ^{[1
]}

Labatut, Vincent ^{[1
]}

机构：

[1] LIA, EA 4128, Avignon, France

来源：

SOCIAL NETWORKS | 2020年 / 60卷

关键词：

Signed graph; Multiplex graph; European Parliament; Graph partitioning; Correlation clustering; Structural balance; COMMUNITY STRUCTURE; STRUCTURAL BALANCE;

D O I：

10.1016/j.socnet.2019.02.001

中图分类号：

Q98 [人类学];

学科分类号：

030303 ;

摘要：

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 with 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.

引用

页码：83 / 102

页数：20