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Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks

Authors:

Ana-Andreea Stoica,

Augustin ChaintreauAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 2594 - 2602

https://doi.org/10.1145/3589334.3645609

Published: 13 May 2024 Publication History

Abstract

In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can reproduce and even amplify bias against minority groups in networks. Yet, their behavior differs: one one hand, we empirically show that PageRank mirrors the degree distribution for most of the ranking positions and it can equalize representation of minorities among the top ranked nodes; on the other hand, we find that HITS amplifies pre-existing bias in homophilic networks through a novel theoretical analysis, supported by empirical results. We find the root cause of bias amplification in HITS to be the level of homophily present in the network, modeled through an evolving network model with two communities. We illustrate our theoretical analysis on both synthetic and real datasets and we present directions for future work.

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Cited By

Bartley NBurghardt KLerman K(2024)Measuring the Echo-Chamber Phenomenon Through Exposure BiasSocial Network Analysis and Mining Applications in Healthcare and Anomaly Detection10.1007/978-3-031-75204-9_13(317-336)Online publication date: 9-Oct-2024
https://doi.org/10.1007/978-3-031-75204-9_13

Index Terms

Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity
  2. World Wide Web
    1. Web searching and information discovery
      1. Web search engines
        Page and site ranking
2. Theory of computation

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

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Cited By

Bartley NBurghardt KLerman K(2024)Measuring the Echo-Chamber Phenomenon Through Exposure BiasSocial Network Analysis and Mining Applications in Healthcare and Anomaly Detection10.1007/978-3-031-75204-9_13(317-336)Online publication date: 9-Oct-2024
https://doi.org/10.1007/978-3-031-75204-9_13

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