Spheres of legislation: polarization and most influential nodes in behavioral context

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 context...

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Bibliographic Details
Published in:Computational social networks 2021-03, Vol.8 (1), Article 14
Main Authors: Phillips, Andrew C., Irfan, Mohammad T., Ostertag-Hill, Luca
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Complex Networks
Context
Data-driven Science
Database Management
Game theory
Industrial and Production Engineering
Industrial Organization
Legislation
Machine learning
Mathematical Models of Cognitive Processes and Neural Networks
Mathematics
Mathematics and Statistics
Media Sociology
Modeling and Theory Building
Network analysis
Nodes
Polarization
Social networks
Spheres
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Summary: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 spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks 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 structural centrality measures fail to capture.
ISSN:2197-4314
2197-4314
DOI:10.1186/s40649-021-00091-2