In this work, we study the behavior of Brazilian politicians and political parties with the help of clustering algorithms for signed social networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the lower house of Brazilian National Congress. We process all available voting data for the period between 2011 and 2016, by considering voting similarities between members of the Congress to define weighted signed links. The solutions obtained by solving Correlation Clustering (CC) problems are the basis for investigating deputies voting networks as well as questions about loyalty, leadership, coalitions, political crisis, and social phenomena such as mediation and polarization.
Cite
@article{arxiv.1609.00767,
title = {Brazilian Congress structural balance analysis},
author = {Mario Levorato and Yuri Frota},
journal= {arXiv preprint arXiv:1609.00767},
year = {2017}
}
Comments
27 pages, 15 tables, 6 figures; entire article was revised, new references added (including international press); correcting typing errors