On Ranking Senators By Their Votes
Mugizi Rwebangira

TL;DR
This paper applies a semi-supervised learning technique using the graph Laplacian to rank US Senators by ideology based on their voting records, demonstrating a novel use of this method for political ranking.
Contribution
It introduces a new application of graph Laplacian-based semi-supervised learning to rank senators by voting similarity, expanding the method's use in political science.
Findings
Effective ranking of senators based on voting data
Demonstrates the versatility of semi-supervised learning techniques
Provides a new tool for political analysis
Abstract
The problem of ranking a set of objects given some measure of similarity is one of the most basic in machine learning. Recently Agarwal proposed a method based on techniques in semi-supervised learning utilizing the graph Laplacian. In this work we consider a novel application of this technique to ranking binary choice data and apply it specifically to ranking US Senators by their ideology.
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Taxonomy
TopicsGame Theory and Voting Systems
