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      Detecting coalitions by optimally partitioning signed networks of political collaboration

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          Abstract

          We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach’s utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number of intra-group negative and inter-group positive edges. We tackle the intensive computations of dense signed networks by providing upper and lower bounds, then solving an optimization model which closes the gap between the two bounds and returns the optimal partitioning of vertices. Our substantive findings suggest that the dominance of an ideologically homogeneous coalition (i.e. partisan polarization) can be a protective factor that enhances legislative effectiveness.

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          Most cited references 45

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          An Efficient Heuristic Procedure for Partitioning Graphs

           B. Kernighan,  S Lin (1970)
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            Structural balance: a generalization of Heider's theory.

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              Social perception and phenomenal causality.

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                Author and article information

                Contributors
                sare618@aucklanduni.ac.nz
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                30 January 2020
                30 January 2020
                2020
                : 10
                Affiliations
                [1 ]ISNI 0000 0001 2033 8007, GRID grid.419511.9, Laboratory of Digital and Computational Demography, , Max Planck Institute for Demographic Research, ; 18057 Rostock, Germany
                [2 ]ISNI 0000 0004 0372 3343, GRID grid.9654.e, School of Computer Science, , University of Auckland, ; 1142 Auckland, New Zealand
                [3 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Psychology, , Michigan State University, ; East Lansing, MI 48824 USA
                Article
                58471
                10.1038/s41598-020-58471-z
                6992702
                32001776
                12c9fe40-4313-4748-8587-384231b31e37
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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