Hostname: page-component-7c8c6479df-7qhmt Total loading time: 0 Render date: 2024-03-18T15:44:40.752Z Has data issue: false hasContentIssue false

Portrait of Political Party Polarization1

Published online by Cambridge University Press:  15 April 2013

JAMES MOODY
Affiliation:
Department of Sociology, Duke University, Durham, NC 27710, USA (e-mail: jmoody77@soc.duke.edu)
PETER J. MUCHA
Affiliation:
Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA

Extract

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).

Type
End Note
Copyright
Copyright © Cambridge University Press 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

1

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

References

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.CrossRefGoogle Scholar
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74 (3), 036104036119.Google Scholar
Poole, K. T. (2012). “Voteview.” Retrieved from http://voteview.com.Google Scholar
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.Google Scholar
White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple Networks I. American Journal of Sociology, 81, 730780.Google Scholar
Supplementary material: File

Moody Supplementary Material

Moody Supplementary Material

Download Moody Supplementary Material(File)
File 163.4 KB