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Spheres of Legislation: Polarization and Most Influential Nodes in Behavioral Context

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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Abstract

Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and apply unsupervised machine learning techniques, such as fuzzy clustering algorithms and generative models, to identify different spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks in order to gain an insight into the role played by the spheres of legislation in various interesting constructs like polarization and most influential nodes? To this end, we apply both game-theoretic and social network analysis techniques. In particular, we show that game-theoretic notion of most influential nodes brings out the strategic aspects of interactions like bipartisan grouping, which typical centrality measures fail to capture. We also show that for the same set of senators, some spheres of legislation are more polarizing than others.

ACP worked on this research as an undergraduate student at Bowdoin. MTI is the corresponding author. LOH is currently an undergraduate student at Bowdoin.

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Acknowledgement

This research was partially supported by NSF grant IIS-1910203. We sincerely thank Dr. Stephen Majercik (Bowdoin College) for reading an earlier draft of this paper and giving us many valuable suggestions. We are also thankful to Drs. Honorio and Ortiz for letting us use their codes [15] and to the anonymous reviewers for their suggestions.

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Authors and Affiliations

  1. Bowdoin College, Brunswick, ME, 04011, USA

    Andrew C. Phillips, Mohammad T. Irfan & Luca Ostertag-Hill

Authors
  1. Andrew C. Phillips
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  2. Mohammad T. Irfan
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  3. Luca Ostertag-Hill
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Corresponding author

Correspondence to Mohammad T. Irfan .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

Appendix

Appendix

Link to the Appendix: http://bit.ly/appendix-spheres-of-legislation.

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Phillips, A.C., Irfan, M.T., Ostertag-Hill, L. (2020). Spheres of Legislation: Polarization and Most Influential Nodes in Behavioral Context. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_6

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