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Portrait of Political Party Polarization1

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  • MOODY, JAMES
  • MUCHA, PETER J.

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).

Suggested Citation

  • Moody, James & Mucha, Peter J., 2013. "Portrait of Political Party Polarization1," Network Science, Cambridge University Press, vol. 1(1), pages 119-121, April.
  • Handle: RePEc:cup:netsci:v:1:y:2013:i:01:p:119-121_00
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    Cited by:

    1. Sandipan Roy & Yves Atchadé & George Michailidis, 2017. "Change point estimation in high dimensional Markov random-field models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1187-1206, September.
    2. Huremović, Kenan & Ozkes, Ali I., 2022. "Polarization in networks: Identification–alienation framework," Journal of Mathematical Economics, Elsevier, vol. 102(C).
    3. Neal, Zachary & Domagalski, Rachel & Yan, Xiaoqin, 2020. "Party Control as a Context for Homophily in Collaborations among US House Representatives, 1981 -- 2015," OSF Preprints qwdxs, Center for Open Science.
    4. Baber, Hasnan, 2020. "Intentions to participate in political crowdfunding- from the perspective of civic voluntarism model and theory of planned behavior," Technology in Society, Elsevier, vol. 63(C).
    5. Natasha Kossovsky & Kathleen M. Carley, 2020. "The collapse of the second Yatsenyuk government: roll call vote and dynamic network analysis," Computational and Mathematical Organization Theory, Springer, vol. 26(1), pages 123-143, March.
    6. Michael W Kraus & Bennett Callaghan, 2014. "Noblesse Oblige? Social Status and Economic Inequality Maintenance among Politicians," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    7. Lee, Jihui & Li, Gen & Wilson, James D., 2020. "Varying-coefficient models for dynamic networks," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    8. Ronald Heijmans & Richard Heuver & Clement Levallois & Iman van Lelyveld, 2014. "Dynamic visualization of large transaction networks: the daily Dutch overnight money market," DNB Working Papers 418, Netherlands Central Bank, Research Department.

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