English

On Ranking Senators By Their Votes

Machine Learning 2009-09-09 v1 Applications
Authors: Mugizi Rwebangira
View on arXiv ↗ PDF ↗

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.

Keywords

decision making under uncertainty

Cite

@article{arxiv.0909.1418,
  title  = {On Ranking Senators By Their Votes},
  author = {Mugizi Rwebangira},
  journal= {arXiv preprint arXiv:0909.1418},
  year   = {2009}
}

Comments

6 pages

Related papers

View all related →
Physics and Society · Physics
Deformed Laplacians and spectral ranking in directed networks
Michaël Fanuel, Johan A. K. Suykens
2017-10-25
Data Structures and Algorithms · Computer Science
Ranking with Partitioning
Samuel Boardman
2026-05-05
Machine Learning · Computer Science
Semi-supervised Learning with Regularized Laplacian
Konstantin Avrachenkov, Pavel Chebotarev, Alexey Mishenin
2015-08-21
Social and Information Networks · Computer Science
$L^\gamma$-PageRank for Semi-Supervised Learning
Esteban Bautista, Patrice Abry, Paulo Gonçalves
2019-03-15
Combinatorics · Mathematics
A Supervised Learning Approach to Rankability
Nathan McJames, David Malone, Oliver Mason
2022-03-15
Machine Learning · Computer Science
Spectral Ranking using Seriation
Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
2016-03-11
Machine Learning · Statistics
Learning to Rank based on Analogical Reasoning
Mohsen Ahmadi Fahandar, Eyke Hüllermeier
2017-12-05
Machine Learning · Statistics
PAC-Bayesian High Dimensional Bipartite Ranking
Benjamin Guedj, Sylvain Robbiano
2019-05-20
Information Retrieval · Computer Science
BiRank: Towards Ranking on Bipartite Graphs
Xiangnan He, Ming Gao, Min-Yen Kan, Dingxian Wang
2017-08-16
Machine Learning · Computer Science
Distance-based Positive and Unlabeled Learning for Ranking
Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park +11
2022-09-29
Computers and Society · Computer Science
A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization
Gabriel Mersy, Vincent Santore, Isaac Rand, Corrine Kleinman +3
2020-08-11
Methodology · Statistics
Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, Øystein Sørensen, Marta Crispino, Arnoldo Frigessi +1
2017-04-28
Machine Learning · Computer Science
How Many Pairwise Preferences Do We Need to Rank A Graph Consistently?
Aadirupa Saha, Rakesh Shivanna, Chiranjib Bhattacharyya
2019-02-13
Information Retrieval · Computer Science
An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction
Guangrui Yang, Lihua Yang, Qing Zhang, Zhihua Yang
2023-07-06
Combinatorics · Mathematics
On the Graph Laplacian and the Rankability of Data
Thomas R. Cameron, Amy N. Langville, Heather C. Smith
2019-12-03
Artificial Intelligence · Computer Science
Consensus measure of rankings
Zhiwei Lin, Yi Li, Xiaolian Guo
2017-09-22
Machine Learning · Computer Science
A Simple Baseline Algorithm for Graph Classification
Nathan de Lara, Edouard Pineau
2018-11-13
Machine Learning · Computer Science
Graph-Based Semi-Supervised Segregated Lipschitz Learning
Farid Bozorgnia, Yassine Belkheiri, Abderrahim Elmoataz
2024-11-06
Data Analysis, Statistics and Probability · Physics
Similarity-Based Classification in Partially Labeled Networks
Qian-Ming Zhang, Ming-Sheng Shang, Linyuan Lu
2010-10-05
Social and Information Networks · Computer Science
Semi-supervised graph labelling reveals increasing partisanship in the United States Congress
Max Glonek, Jonathan Tuke, Lewis Mitchell, Nigel Bean
2019-06-18
Computation and Language · Computer Science
Answering questions by learning to rank -- Learning to rank by answering questions
George-Sebastian Pîrtoacă, Traian Rebedea, Stefan Ruseti
2019-11-15
Machine Learning · Computer Science
Approximate Ranking from Pairwise Comparisons
Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright
2018-01-08
Machine Learning · Computer Science
Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent
Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco
2023-09-26
Machine Learning · Computer Science
Representation Learning by Ranking across multiple tasks
Lifeng Gu
2025-04-22
Computer Vision and Pattern Recognition · Computer Science
MidRank: Learning to rank based on subsequences
Basura Fernando, Efstratios Gavves, Damien Muselet, Tinne Tuytelaars
2015-12-01
R2 v1 2026-06-21T13:43:48.576Z