Community Detection and Analysis of Political Alliances in the Brazilian Congress Voting Network
Resumo
Context: Network analysis in legislative studies reveals hidden alliances and ideological shifts, helping to understand evolving party behavior and dynamics in democratic systems. Problem: Many voting network studies lack filtering for non-polarized propositions, weakening ideological clarity and modularity. Without edge pruning, irrelevant connections clutter networks, reducing accuracy in detecting true political alliances and dynamics. Solution: This research uses the Leiden algorithm on polarized propositions with edge pruning to detect cohesive communities in Brazil’s Congress, enhancing modularity and revealing true political dynamics. IS Theory: This study uses Social Network Theory to examine voting alliances and Institutional Theory to analyze how norms shape party behavior in Brazil’s Congress, offering insights into political behavior within complex systems. Method: This study uses a quantitative, descriptive approach with network analysis to examine Brazilian congressional voting patterns. Using public data and the Leiden algorithm, it identifies political communities, enhanced by filtering polarized votes and preprocessing to clarify network structure. Summary of Results: Our analysis of Brazilian congressional alliances showed a 10.95% modularity improvement using the Leiden algorithm with edge pruning, revealing clear ideological divides and a dynamic "swing" community influencing alliances in key years. Contributions and Impact on IS: This research advances IS by using network analysis on political voting to clarify alliances. Techniques like polarized data filtering and network optimization provide valuable tools for analyzing complex networks in IS.
Referências
Salah Boudebza, Karim Boujenfa, Badr Hamdaoui, and Mohamed Kharoune. 2022. An Approach for Detecting Dynamic Communities in Social Networks. arXiv preprint 2212.02383 (2022). [link]
Ana Caroline Medeiros Brito, Filipi Nascimento Silva, and Diego Raphael Amancio. 2020. A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies. PLoS ONE 15, 3 (2020), e0229928. DOI: 10.1371/journal.pone.0229928
J. Bryden and E. Silverman. 2019. Underlying socio-political processes behind the 2016 US election. PLoS ONE 14, 4 (2019), e0214854. DOI: 10.1371/journal.pone.0214854
D. Cherepnalkoski, A. Karpf, I. Mozetič, and M. Grčcar. 2016. Cohesion and coalition formation in the European Parliament: roll-call votes and Twitter activities. PLoS ONE 11, 11 (2016), e0166586. DOI: 10.1371/journal.pone.0166586
J. Faustino, H. Barbosa, E. Ribeiro, and R. Menezes. 2019. A data-driven network approach for characterization of political parties’ ideology dynamics. Applied Network Science 4, 1 (2019), 48. DOI: 10.1007/s41109-019-0161-0
C.H.G. Ferreira, B. de Sousa Matos, and J.M. Almeira. 2018. Analyzing Dynamic Ideological Communities in Congressional Voting Networks. In International Conference on Social Informatics. Springer, 257–273.
M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 12 (2002), 7821–7826. DOI: 10.1073/pnas.122653799
Harold B. Lee Library. 2024. Information Systems Theories. [link]. Accessed: 18-Oct-2024.
M. Levorato and Y. Frota. 2017. Brazilian Congress structural balance analysis. Journal of Interdisciplinary Methodologies and Issues in Science 2 (2017).
Carlo Dal Maso, Gabriele Pompa, Michelangelo Puliga, Gianni Riotta, and Alessandro Chessa. 2014. Voting Behavior, Coalitions and Government Strength through a Complex Network Analysis. PLoS ONE 9, 12 (2014), e116046. DOI: 10.1371/journal.pone.0116046
H.P.M. Melo, S.D. Reis, A.A. Moreira, H.A. Makse, and J.S. Jr. Andrade. 2018. The price of a vote: Diseconomy in proportional elections. PLoS ONE 13, 8 (2018), e0201654. DOI: 10.1371/journal.pone.0201654
J. Moody and P.J. Mucha. 2013. Portrait of Political Party Polarization. Network Science 1, 1 (2013), 119–121. DOI: 10.1017/nws.2012.3
NetworkX Developers. 2024. NetworkX Documentation. [link] Accessed: 18-Oct-2024.
M. Newman. 2018. Networks: an introduction. Oxford University Press.
V. Traag, L. Waltman, and N.J. van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9 (2019). DOI: 10.1038/s41598-019-41695-z
F. Zhou, S. Mahler, and H. Toivonen. 2012. Simplification of networks by edge pruning. In Bisociative Knowledge Discovery. Springer, 179–198.