2019/05/31 by Ludwing Marenco, Humberto A. Carmona, Felipe Maciel Cardoso +5
Economics, Econometrics and Finance · Engineering · Physics and Astronomy · #Biology #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Complex network #Computer science #Electrical engineering #Engineering #Law #Legislature #Network analysis #Opinion Dynamics and Social Influence #Political science #World Wide Web #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0226504
published as PLoS ONE 15(2): e0226504 (2020) · 11 pages, 4 Figures
openalex publication_date 2020/02/05 · arxiv created 2020/02/06 · arxiv updated 2020/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The follow up of Representative behavior after elections is imperative for a democratic Representative system, at the very least to punish betrayal with no re-election. Our goal was to show how to follow Representatives' and how to show behavior in real situations and observe trends in political crises including the onset of game changing political instabilities. We used correlation and correlation distance matrices of Brazilian Representative votes during four presidential terms. Re-ordering these matrices with Minimal Spanning Trees displays the dynamical formation of clusters for the sixteen year period, which includes one Presidential impeachment. The reordered matrices, colored by correlation strength and by the parties clearly show the origin of observed clusters and their evolution over time. When large clusters provide government support cluster breaks, political instability arises, which could lead to an impeachment, a trend we observed three years before the Brazilian President was impeached. We believe this method could be applied to foresee other political storms.