research-article

Fairness in Social Influence Maximization

Authors:

Ana-Andreea Stoica,

Augustin ChaintreauAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 569 - 574

https://doi.org/10.1145/3308560.3317588

Published: 13 May 2019 Publication History

Abstract

Algorithms for social influence maximization have been extensively studied for the purpose of strategically choosing an initial set of individuals in a social network from which information gets propagated. With many applications in advertisement, news spread, vaccination, and online trend-setting, this problem is a central one in understanding how information flows in a network of individuals. As human networks may encode historical biases, algorithms performing on them might capture and reproduce such biases when automating outcomes.

In this work, we study the social influence maximization problem for the purpose of designing fair algorithms for diffusion, aiming to understand the effect of communities in the creation of disparate impact among network participants based on demographic attributes (gender, race etc). We propose a set of definitions and models for assessing the fairness-utility tradeoff in designing algorithms that maximize influence through a mathematical model of diffusion and an empirical analysis of a collected dataset from Instagram. Our work shows that being feature-aware can lead to more diverse outcomes in outreach and seed selection, as well as better efficiency, than being feature-blind.

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Cited By

Mazzoni FFailla ARossetti G(2025)FairNet: A Genetic Framework to Reduce Marginalization in Social NetworksSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_9(139-154)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_9
Zhang YMukhopadhyay RChaintreau A(2024)Network Fairness Ambivalence: When does social network capital mitigate or amplify unfairness?Proceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36560178:2(1-28)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3656017
Saxena AFletcher GPechenizkiy M(2024)FairSNA: Algorithmic Fairness in Social Network AnalysisACM Computing Surveys10.1145/365371156:8(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653711
Show More Cited By

Index Terms

Fairness in Social Influence Maximization
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Mazzoni FFailla ARossetti G(2025)FairNet: A Genetic Framework to Reduce Marginalization in Social NetworksSocial Networks Analysis and Mining10.1007/978-3-031-78541-2_9(139-154)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-3-031-78541-2_9
Zhang YMukhopadhyay RChaintreau A(2024)Network Fairness Ambivalence: When does social network capital mitigate or amplify unfairness?Proceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36560178:2(1-28)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3656017
Saxena AFletcher GPechenizkiy M(2024)FairSNA: Algorithmic Fairness in Social Network AnalysisACM Computing Surveys10.1145/365371156:8(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653711
Balepur NSundaram H(2024)Intervening to Increase Community Trust for Fair Network OutcomesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659008(1827-1837)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659008
Zhao ZLi WMa JJiang JBai Q(2024)Fair Influence Maximization in Social Networks: A Group-Fairness-aware Multi-Objective Grey Wolf Optimizer*2024 IEEE International Conference on Agents (ICA)10.1109/ICA63002.2024.00026(88-93)Online publication date: 4-Dec-2024
https://doi.org/10.1109/ICA63002.2024.00026
Zhang CZhou JWei WShi Y(2024)Order-Sensitive Competitive Revenue Maximization for Viral Marketing in Social NetworksInformation Sciences10.1016/j.ins.2024.121474(121474)Online publication date: Sep-2024
https://doi.org/10.1016/j.ins.2024.121474
Dong YMa JWang SChen CLi J(2023)Fairness in Graph Mining: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326559835:10(10583-10602)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3265598
Razaghi BRoayaei MCharkari N(2023)On the Group-Fairness-Aware Influence Maximization in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.319809610:6(3406-3414)Online publication date: Dec-2023
https://doi.org/10.1109/TCSS.2022.3198096
Gong HGuo C(2023)Influence maximization considering fairness: A multi-objective optimization approach with prior knowledgeExpert Systems with Applications10.1016/j.eswa.2022.119138214(119138)Online publication date: Mar-2023
https://doi.org/10.1016/j.eswa.2022.119138
Saxena AGutiérrez Bierbooms CPechenizkiy M(2023)Fairness-aware fake news mitigation using counter information propagationApplied Intelligence10.1007/s10489-023-04928-353:22(27483-27504)Online publication date: 12-Sep-2023
https://dl.acm.org/doi/10.1007/s10489-023-04928-3
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