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Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention.
Finding a maximum density subgraph
A. V. Goldberg · 1984
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Analyzing the structure of large graphs
Ravi Kannan and V Vinay · 1999
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Spectral partitioning of random graphs
Frank McSherry · 2001
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Discovering large dense subgraphs in massive graphs
David Gibson, Ravi Kumar, and Andrew Tomkins · 2005
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The compositional impact of team diversity on performance: Theoretical considerations
Sujin Horwitz · 2005
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Motifcut: regulatory motifs finding with maximum density subgraphs
Eugene Fratkin, Brian T Naughton, Douglas L Brutlag, and Serafim Batzoglou · 2006
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Finding a team of experts in social networks
Theodoros Lappas, Kun Liu, and Evimaria Terzi · 2009
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Detecting high log-densities: an O ( n 1/4 {}^{\mbox{1/4}} ) approximation for densest k -subgraph
Aditya Bhaskara, Moses Charikar, Eden Chlamtac, Uriel Feige, and Aravindan Vijayaraghavan · 2010
Earlier work this paper cites.
Graph expansion and the unique games conjecture
Prasad Raghavendra and David Steurer · 2010
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Graph expansion and the unique games conjecture
Prasad Raghavendra and David Steurer · 2010
Earlier work this paper cites.
The community-search problem and how to plan a successful cocktail party
Mauro Sozio and Aristides Gionis · 2010
Earlier work this paper cites.
k-nn as an implementation of situation testing for discrimination discovery and prevention
Binh Luong Thanh, Salvatore Ruggieri, and Franco Turini · 2011
Earlier work this paper cites.
Polynomial integrality gaps for strong SDP relaxations of densest k -subgraph
Aditya Bhaskara, Moses Charikar, Aravindan Vijayaraghavan, Venkatesan Guruswami, and Yuan Zhou · 2012
Earlier work this paper cites.
Piggybacking on social networks
Aristides Gionis, Flavio Junqueira, Vincent Leroy, Marco Serafini, and Ingmar Weber · 2013
Cited alongside, same era.
Multi-agent team formation: diversity beats strength?
Leandro Soriano Marcolino, Albert Xin Jiang, and Milind Tambe · 2013
Cited alongside, same era.
Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees
Charalampos E. Tsourakakis, Francesco Bonchi, Aristides Gionis, Francesco Gullo, and Maria A. Tsiarli · 2013
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Is the internet causing political polarization? evidence from demographics
Levi Boxell, Matthew Gentzkow, and Jesse Shapiro · 2017
Multiwinner voting with fairness constraints
L. Elisa Celis, Lingxiao Huang, and Nisheeth K. Vishnoi · 2018
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Ranking with fairness constraints
L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi · 2018
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Minimizing polarization and disagreement in social networks
Cameron Musco, Christopher Musco, and Charalampos E. Tsourakakis · 2018
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Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel Bakker, Bernardo Garcia-Bulle, and Alex Pentland · 2018
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Privacy preserving clustering with constraints
Clemens Rösner and Melanie Schmidt · 2018
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The price of fair PCA: one extra dimension
Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie H. Morgenstern, Mohit Singh, and Santosh Vempala · 2018
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Cited alongside, same era.
Metrics for community analysis: A survey
Tanmoy Chakraborty, Ayushi Dalmia, Animesh Mukherjee, and Niloy Ganguly · 2017
Cited alongside, same era.
Fair clustering through fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, and Sergei Vassilvitskii · 2017
Cited alongside, same era.
Almost-polynomial ratio eth-hardness of approximating densest k-subgraph
Pasin Manurangsi · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2017
Cited alongside, same era.
From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, and Adrian Weller · 2017
Cited alongside, same era.
Later among the works it cites.
Scalable fair clustering
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner · 2019
Closest in time.
Fair algorithms for clustering
Suman Kalyan Bera, Deeparnab Chakrabarty, Nicolas Flores, and Maryam Negahbani · 2019
Closest in time.
Coresets for clustering with fairness constraints
Lingxiao Huang, Shaofeng H.-C. Jiang, and Nisheeth K. Vishnoi · 2019
Closest in time.
Guarantees for spectral clustering with fairness constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern · 2019
Closest in time.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Fair coresets and streaming algorithms for fair k-means
Melanie Schmidt, Chris Schwiegelshohn, and Christian Sohler · 2019
Closest in time.
Multi-criteria dimensionality reduction with applications to fairness
Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H. Morgenstern, and Santosh S. Vempala · 2019
Closest in time.