On Ranking Senators By Their Votes

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.