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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.
Keywords
influence in networks, machine learning and networks, computational game theory, graphical games
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CURRENT STATUS: Published
View the published article at Computational Social Networks.
Version 2
Posted 19 Nov, 2020
No community comments so far
Editorial decision: Accept
On 17 Jan, 2021
Review #2 received
Received 20 Dec, 2020
Review #1 received
Received 09 Dec, 2020
Reviewer #2 agreed
On 21 Nov, 2020
Reviewer #1 agreed
On 21 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Editor assigned
On 16 Nov, 2020
Submission checks complete
On 16 Nov, 2020
Editor invited
On 16 Nov, 2020
No comments provided
Review #2 received
Received 07 Sep, 2020
Editorial decision: Minor Revision
On 07 Sep, 2020
Review #1 received
Received 19 Aug, 2020
Reviewer #2 agreed
On 25 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Editor assigned
On 30 Jun, 2020
First submitted
On 29 Jun, 2020
Submission checks complete
On 29 Jun, 2020
Editor invited
On 29 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
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See the published version of this preprint at Computational Social Networks.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Computational Social Networks.
Version 2
Posted 19 Nov, 2020
No community comments so far
Editorial decision: Accept
On 17 Jan, 2021
Review #2 received
Received 20 Dec, 2020
Review #1 received
Received 09 Dec, 2020
Reviewer #2 agreed
On 21 Nov, 2020
Reviewer #1 agreed
On 21 Nov, 2020
Reviewers invited
Invitations sent on 17 Nov, 2020
Editor assigned
On 16 Nov, 2020
Submission checks complete
On 16 Nov, 2020
Editor invited
On 16 Nov, 2020
No comments provided
Review #2 received
Received 07 Sep, 2020
Editorial decision: Minor Revision
On 07 Sep, 2020
Review #1 received
Received 19 Aug, 2020
Reviewer #2 agreed
On 25 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Editor assigned
On 30 Jun, 2020
First submitted
On 29 Jun, 2020
Submission checks complete
On 29 Jun, 2020
Editor invited
On 29 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Computer Architecture and Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Computational Social Networks.
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
This is a list of supplementary files associated with this preprint. Click to download.
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