# Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes

arXiv: https://arxiv.org/abs/1811.10337 · HTML: https://mlsanity.com/paper/1811.10337/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Whole-Graph Representation Learning For the Classification of Signed Networks](https://mlsanity.com/paper/2409.20073/) (2024)
- [Signed graphs in data sciences via communicability geometry](https://mlsanity.com/paper/2403.07493/) (2024)

## Abstract (arXiv)

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.