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Community structure in the United Nations General Assembly

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  • Macon, Kevin T.
  • Mucha, Peter J.
  • Porter, Mason A.

Abstract

We study the community structure of networks representing voting on resolutions in the United Nations General Assembly. We construct networks from the voting records of the separate annual sessions between 1946 and 2008 in three different ways: (1) by considering voting similarities as weighted unipartite networks; (2) by considering voting similarities as weighted, signed unipartite networks; and (3) by examining signed bipartite networks in which countries are connected to resolutions. For each formulation, we detect communities by optimizing network modularity using an appropriate null model. We compare and contrast the results that we obtain for these three different network representations. We thereby illustrate the need to consider multiple resolution parameters and explore the effectiveness of each network representation for identifying voting groups amidst the large amount of agreement typical in General Assembly votes.

Suggested Citation

  • Macon, Kevin T. & Mucha, Peter J. & Porter, Mason A., 2012. "Community structure in the United Nations General Assembly," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 343-361.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:1:p:343-361
    DOI: 10.1016/j.physa.2011.06.030
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    References listed on IDEAS

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    1. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517, Decembrie.
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    Cited by:

    1. 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.
    2. Marya Bazzi & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2014. "Community detection in temporal multilayer networks, with an application to correlation networks," Papers 1501.00040, arXiv.org, revised Dec 2017.
    3. Mario Levorato & Rosa Figueiredo & Yuri Frota & Lúcia Drummond, 2017. "Evaluating balancing on social networks through the efficient solution of correlation clustering problems," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 467-498, December.
    4. Figueiredo, Rosa & Frota, Yuri, 2014. "The maximum balanced subgraph of a signed graph: Applications and solution approaches," European Journal of Operational Research, Elsevier, vol. 236(2), pages 473-487.

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