Uncovering Political Alliances and Their Temporal Evolution in the Brazilian Congress Voting Network
DOI:
https://doi.org/10.5753/isys.2026.6878Keywords:
Community Detection, Network Modularity, Political Polarization, Graph Theory, Temporal Analysis, Decision Support; Transparency, GovernanceAbstract
Network analysis reveals alliances, ideological shifts, and party dynamics, and can support transparency and evidence-based decision-making in democratic governance. Many studies ignore non-polarized filtering and edge pruning, which lowers modularity and clarity. This research applies the Leiden algorithm to polarized votes in Brazil's Congress, using edge pruning to detect cohesive communities. Grounded in Social Network Theory and Institutional Theory, it examines how alliances form under institutional norms. Using public roll-call voting data (formal legislative outcomes), we filtered polarized votes and optimized the network. To quantify temporal stability and evolution, we introduce three temporal metrics: the Herfindahl-Hirschman Index (HHI) for community concentration, the Adjusted Rand Index (ARI) for community similarity across years, and Edge Volatility for network structural changes. Additionally, we employ backbone comparison using the backbone extraction to identify statistically significant network connections. This work advances Information Systems by demonstrating how filtering and optimization expose hidden structures and political dynamics, while providing quantitative measures of temporal community evolution. Results show a 10.95% modularity gain compared to the backbone extraction and reveal clear ideological blocs and a "swing" community, which can be used by journalists, oversight institutions, and citizens to monitor coalition shifts and accountability over time.
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