Portrait of Political Party Polarization1

James Moody and Peter J. Mucha

Network Science, 2013, vol. 1, issue 1, 119-121

Abstract: 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).

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:netsci:v:1:y:2013:i:01:p:119-121_00

Access Statistics for this article

More articles in Network Science from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().