IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i1p343-361.html
   My bibliography  Save this article

Community structure in the United Nations General Assembly

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111004778
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2011.06.030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diego Garlaschelli & Maria I. Loffredo, 2007. "Effects of network topology on wealth distributions," Papers 0711.4710, arXiv.org, revised Jan 2008.
    2. Ya-Chun Gao & Zong-Wen Wei & Bing-Hong Wang, 2013. "Dynamic Evolution Of Financial Network And Its Relation To Economic Crises," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-10.
    3. Hutzler, S. & Sommer, C. & Richmond, P., 2016. "On the relationship between income, fertility rates and the state of democracy in society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 9-18.
    4. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    5. Tsekeris, Theodore, 2016. "Interregional trade network analysis for road freight transport in Greece," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 132-148.
    6. F. Daolio & M. Tomassini & K. Bitkov, 2011. "The Swiss board directors network in 2009," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 82(3), pages 349-359, August.
    7. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    8. Shekhtman, Louis M. & Danziger, Michael M. & Havlin, Shlomo, 2016. "Recent advances on failure and recovery in networks of networks," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 28-36.
    9. Deyun Zhong & Lixue Wen & Lin Bi & Yulong Liu, 2024. "An Efficient and Automatic Simplification Method for Arbitrary Complex Networks in Mine Ventilation," Mathematics, MDPI, vol. 12(18), pages 1-17, September.
    10. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    11. Wang, Zhenggang & Szeto, K.Y., 2010. "Structure profile of complex networks by a model of precipitation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2318-2324.
    12. Mohammad Ariapour & Ehsan Nedaaee Oskoee, 2013. "Constructing Scale-Free Networks With A Given Cluster Coefficient," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-14.
    13. Roy Cerqueti & Giulia Rotundo & Marcel Ausloos, 2021. "Tsallis entropy for cross-shareholding network configurations," Papers 2109.04214, arXiv.org.
    14. Danilo Delpini & Stefano Battiston & Guido Caldarelli & Massimo Riccaboni, 2019. "Systemic risk from investment similarities," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.
    15. Roberto Antonietti & Giulia De Masi & Giorgio Ricchiuti, 2020. "Linking FDI Network Topology with the Covid-19 Pandemic," Papers in Evolutionary Economic Geography (PEEG) 2054, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Nov 2020.
    16. Brancaccio, Emiliano & Giammetti, Raffaele & Lopreite, Milena & Puliga, Michelangelo, 2019. "Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 55-66.
    17. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, June.
    18. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    19. Andrey Sokolov & Rachel Webster & Andrew Melatos & Tien Kieu, 2012. "Loan and nonloan flows in the Australian interbank network," Papers 1202.3182, arXiv.org.
    20. Ding, Jie & Wen, Changyun & Li, Guoqi, 2017. "Key node selection in minimum-cost control of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 251-261.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:1:p:343-361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.