Modeling Dynamic Ideological Behavior in Political Networks


  • Carlos Henrique Gomes Ferreira Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
  • Fabricio Murai Ferreira Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
  • Breno de Sousa Matos Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
  • Jussara Marques de Almeida Department of Computer Science, Universidade Federal de Minas Gerais, Brazil



In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. Whereas the former is an example of a  highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose  a temporal ideological space model, based on temporal vertice embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members' voting patterns.   Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.


P. Agathangelou, I. Katakis, L. Rori, D. Gunopulos, and B. Richards. “Understanding Online Political Networks: The Case of the Far-Right and Far-Left in Greeceâ€. In: International Conference on Social Informatics. Springer. 162–177

B. Ames. The deadlock of democracy in Brazil. University of Michigan Press.

C. Andris, D. Lee, M. J. Hamilton, M. Martino, C. E. Gunning, and J. A. Selden. “The Rise of Partisanship and Super-Cooperators in the U.S. House of Representativesâ€. PLOS ONE. 10(4): 1–14.

N. Arinik, R. Figueiredo, and V. Labatut. “Signed Graph Analysis for the Interpretation of Voting Behaviorâ€. International Workshop on Social Network Analysis and Digital Humanities.

D. Baldassarri and A. Gelman. “Partisans without constraint: Political polarization and trends in American public opinionâ€. American Journal of Sociology. 114(2): 408–446.

R. Bamler and S. Mandt. “Dynamic Word Embeddingsâ€. In: Proceedings of the 34th International Conference on Machine Learning. Ed. by D. Precup and Y. W. Teh. Vol. 70. Proceedings of Machine Learning Research. PMLR. 380–389.

A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra. “Clustering on the unit hypersphere using von Mises-Fisher distributionsâ€. Journal of Machine Learning Research. 6(Sep): 1345–1382.

BBC. Brazil profile - Timeline. url: com/news/world-latin- america- 19359111 (accessed on 05/15/2018).

V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. “Fast unfolding of communities in large networksâ€. Journal of statistical mechanics: theory and experiment. 2008(10).

I. Budge and M. J. Laver. Party policy and government coalitions. Springer.

Q. Cai, L. Ma, M. Gong, and D. Tian. “A survey on network community detection based on evolutionary computationâ€. International Journal of Bio-Inspired Computation. 8(2): 84–98.

D. Cherepnalkoski, A. Karpf, I. MozetiÄ, and M. GrÄar. “Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activitiesâ€. PLOS ONE. 11(11): 1–27.

P. Cui, X. Wang, J. Pei, and W. Zhu. “A survey on network embeddingâ€. IEEE Transactions on Knowledge and Data Engineering.

C. Dal Maso, G. Pompa, M. Puliga, G. Riotta, and A. Chessa. “Voting Behavior, Coalitions and Government Strength through a Complex Network Analysisâ€. PLOS ONE. 9(Dec.): 1–13.

K. Darwish, W. Magdy, and T. Zanouda. “Trump vs. Hillary: What Went Viral During the 2016 US Presidential Electionâ€. In: International Conference on Social Informatics. Springer. 143–161.

M. A. Davenport and J. Romberg. “An Overview of LowRank Matrix Recovery From Incomplete Observationsâ€. IEEE Journal of Selected Topics in Signal Processing. 10(4): 608–622. issn: 1932-4553.

D. Easley and J. Kleinberg. Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.

V. Eidelman, A. Kornilova, and D. Argyle. “How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Levelâ€. In: Proceedings of the 27th International Conference on Computational Linguistics.

M. P. Fiorina and S. J. Abrams. “Political Polarization in the American Publicâ€. Annual Review of Political Science. 11(1): 563–588.

S. Fortunato. “Community detection in graphsâ€. Physics Reports. 486(3): 75–174. issn: 0370-1573.

P. Goyal, S. R. Chhetri, and A. Canedo. “dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learningâ€. arXiv: 1809.02657 [cs.SI].

P. Goyal, N. Kamra, X. He, and Y. Liu. “DynGEM: Deep Embedding Method for Dynamic Graphsâ€. In: 3rd International Workshop on Representation Learning for Graphs (ReLiG) at IJCAI ’18.

M. S. Granovetter. “The strength of weak tiesâ€. In: Social networks. Elsevier. 347–367.

A. Grover and J. Leskovec. “node2vec: Scalable feature learning for networksâ€. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 855–864.

S. Hix, A. Noury, and G. Roland. “Power to the parties: cohesion and competition in the European Parliament, 1979–2001â€. British Journal of Political Science. 35(2): 209–234.

X. Huang, J. Li, and X. Hu. “Accelerated attributed network embeddingâ€. In: Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM. 633–641.

J. J. Jones, J. E. Settle, R. M. Bond, C. J. Fariss, C. Marlow, and J. H. Fowler. “Inferring Tie Strength from Online Directed Behaviorâ€. PLOS ONE. 8(Jan.): 1–6.

A. Kornilova, D. Argyle, and V. Eidelman. “Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Predictionâ€. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Vol. 2. Melbourne, Australia: Association for Computational Linguistics. 510–515.

M. Kusner, Y. Sun, N. Kolkin, and K. Weinberger. “From word embeddings to document distancesâ€. In: International Conference on Machine Learning. 957–966.

B. E. Lauderdale and A. Herzog. “Measuring political positions from legislative speechâ€. Political Analysis. 24(3): 374–394.

M. Levorato and Y. Frota. “Brazilian Congress structural balance analysisâ€. Journal of Interdisciplinary Methodologies and Issues in Science. Graphs & Social Systems.

O. Levy and Y. Goldberg. “Neural word embedding as implicit matrix factorizationâ€. In: Advances in neural information processing systems. 2177–2185.

J. Li, H. Dani, X. Hu, J. Tang, Y. Chang, and H. Liu. “Attributed Network Embedding for Learning in a Dynamic Environmentâ€. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. CIKM ’17. ACM. 387–396.

L. v. d. Maaten and G. Hinton. “Visualizing data using t-SNEâ€. Journal of machine learning research. 9(Nov): 2579–2605.

S. Mahdavi, S. Khoshraftar, and A. An. “dynnode2vec:Scalable Dynamic Network Embeddingâ€. In: Workshopon Advances in High Dimensional Big Data at IEEE BigData ’18

S. Mainwaring and M. S. Shugart. Presidentialism and democracy in Latin America. Cambridge University Press.

T. E. Mann and N. J. Ornstein. It’s even worse than it looks: How the American constitutional system collided with the new politics of extremism. Basic Books.

J. McGee, J. Caverlee, and Z. Cheng. “Location Prediction in Social Media Based on Tie Strengthâ€. In: Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management. CIKM ’13. San Francisco, California, USA: ACM. 459–468. isbn: 978-1- 4503-2263-8.

I. Mendonça, A. Trouve, and A. Fukuda. “Exploring the Importance of Negative Links Through the European Parliament Social Graphâ€. In: Proceedings of the 2017 International Conference on E-Society, E-Education and E-Technology. ICSET 2017.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. “Distributed Representations of Words and Phrases and their Compositionalityâ€. In: Advances in Neural Information Processing Systems 26. 3111–3119.

J. Moody and P. J. Mucha. “Portrait of Political Party Polarizationâ€. Network Science. 1(1): 119–121.

M. E. J. Newman. “Modularity and community structure in networksâ€. Proceedings of the National Academy of Sciences. 103(23): 8577–8582.

G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim. “Continuous-Time Dynamic Network Embeddingsâ€. In: Companion Proceedings of the The Web Conference 2018. WWW ’18. 969–976.

V.-A. Nguyen, J. Boyd-Graber, P. Resnik, and K. Miler. “Tea party in the house: A hierarchical ideal point topic model and its application to Republican legislators in the 112th congressâ€. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Vol. 1. 1438–1448.

M. Plantié and M. Crampes. “Survey on social community detectionâ€. In: Social media retrieval. Springer. 65– 85.

M. A. Porter, P. J. Mucha, M. E. J. Newman, and C. M. Warmbrand. “A network analysis of committees in the U.S. House of Representativesâ€. Proceedings of the National Academy of Sciences. 102(20): 7057–7062.

S. A. Rice. “The behavior of legislative groups: a method of measurementâ€. Political Science Quarterly. 40(1): 60– 72.

G. Rossetti and R. Cazabet. “Community Discovery in Dynamic Networks: A Surveyâ€. ACM Comput. Surv. 51(2).

T. Sakamoto and H. Takikawa. “Cross-national measurement of polarization in political discourse: Analyzing floor debate in the US the Japanese legislaturesâ€. In: Big Data (Big Data), 2017 IEEE International Conference on. IEEE. 3104–3110.

G. Sartori. Parties and party systems: A framework for analysis. ECPR press.

C. E. Shannon. “A mathematical theory of communicationâ€. ACM SIGMOBILE Mobile Computing and Communications Review. 5(1): 3–55.

P. O. S. Vaz de Melo. “How Many Political Parties Should Brazil Have? A Data-Driven Method to Assess and Reduce Fragmentation in Multi-Party Political Systemsâ€. PLOS ONE. 10(10): 1–24.

N. X. Vinh, J. Epps, and J. Bailey. “Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chanceâ€. Journal of Machine Learning Research. 11(Oct): 2837–2854.

Vox. Brazil’s political crisis, explained. url: https : / / www. vox. com / 2016 / 4 / 21 / 11451210 / dilma - rousseff - impeachment (accessed on 05/15/2018).

Y. Wang, Y. Feng, Z. Hong, R. Berger, and J. Luo. “How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followersâ€. In: International Conference on Social Informatics. Springer. 440–456.

A. S. Waugh, L. Pei, J. H. Fowler, P. J. Mucha, and M. A. Porter. “Party polarization in Congress: A social networks approachâ€. arXiv preprint arXiv:0907.3509.

W. Wei and K. M. Carley. “Measuring temporal patterns in dynamic social networksâ€. ACM Transactions on Knowledge Discovery from Data (TKDD). 10(1): 9.

J. Wiese, J.-K. Min, J. I. Hong, and J. Zimmerman. “"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate Tie Strengthâ€. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing. CSCW ’15.

Z. Yao, Y. Sun, W. Ding, N. Rao, and H. Xiong. “Dynamic Word Embeddings for Evolving Semantic Discoveryâ€. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. WSDM ’18.

H. Yu, C. Hsieh, S. Si, and I. Dhillon. “Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systemsâ€. In: 2012 IEEE 12th International Conference on Data Mining. 765–774. doi: 10.1109/ICDM.2012.168.

L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan. “Scalable temporal latent space inference for link prediction in dynamic social networksâ€. IEEE Transactions on Knowledge and Data Engineering. 28(10): 2765–2777.