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Portrait of Political Party Polarization1

  • JAMES MOODY (a1) and PETER J. MUCHA (a2)
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To find out, we measure co-voting similarity networks in the US Senate and trace individual careers over time. Standard network visualization tools fail on dense highly clustered networks, so we used two aggregation strategies to clarify positional mobility over time. First, clusters of Senators who often vote the same way capture coalitions, and allow us to measure polarization quantitatively through modularity (Newman, 2006; Waugh et al., 2009; Poole, 2012). Second, we use role-based blockmodels (White et al., 1976) to identify role positions, identifying sets of Senators with highly similar tie patterns. Our partitioning threshold for roles is stringent, generating many roles occupied by single Senators. This combination allows us to identify movement between positions over time (specifically, we used the Kernighan–Lin improvement of a Louvain method greedy partitioning algorithm for modularity [Blondel et al., 2008], and CONCOR with an internal similarity threshold for roles; see Supplementary materials for details).

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1

Thanks to Joshua Medelsohn and participants of the Political Networks Conference (June 2010, Duke) for comments.

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Blondel, V. D., Guillaume, J. L., Lambiotte, R. E., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, 10008.
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74 (3), 036104036119.
Poole, K. T. (2012). “Voteview.” Retrieved from http://voteview.com.
Waugh, A. S., Pei, L., Fowler, J. H., Mucha, P. J., & Porter, M. A. (2009). Party polarization in Congress: A Network Science Approach SSRN. Retrieved from http://ssrn.com/abstract=1437055.
White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple Networks I. American Journal of Sociology, 81, 730780.
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Network Science
  • ISSN: 2050-1242
  • EISSN: 2050-1250
  • URL: /core/journals/network-science
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