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 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 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 structural centrality measures fail to capture.
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Posted 19 Nov, 2020
Received 20 Dec, 2020
Received 09 Dec, 2020
On 21 Nov, 2020
On 21 Nov, 2020
Invitations sent on 17 Nov, 2020
On 16 Nov, 2020
On 16 Nov, 2020
On 16 Nov, 2020
Received 07 Sep, 2020
On 07 Sep, 2020
Received 19 Aug, 2020
On 25 Jul, 2020
On 22 Jul, 2020
Invitations sent on 13 Jul, 2020
On 30 Jun, 2020
On 29 Jun, 2020
On 29 Jun, 2020
On 29 Jun, 2020
Spheres of Legislation: Polarization and Most Influential Nodes in Behavioral Context
Posted 19 Nov, 2020
Received 20 Dec, 2020
Received 09 Dec, 2020
On 21 Nov, 2020
On 21 Nov, 2020
Invitations sent on 17 Nov, 2020
On 16 Nov, 2020
On 16 Nov, 2020
On 16 Nov, 2020
Received 07 Sep, 2020
On 07 Sep, 2020
Received 19 Aug, 2020
On 25 Jul, 2020
On 22 Jul, 2020
Invitations sent on 13 Jul, 2020
On 30 Jun, 2020
On 29 Jun, 2020
On 29 Jun, 2020
On 29 Jun, 2020
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 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 structural centrality measures fail to capture.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.