skip to main content
10.1145/3366423.3380275acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

Seeding Network Influence in Biased Networks and the Benefits of Diversity

Published: 20 April 2020 Publication History

Abstract

The problem of social influence maximization is widely applicable in designing viral campaigns, news dissemination, or medical aid. State-of-the-art algorithms often select “early adopters” that are most central in a network unfortunately mirroring or exacerbating historical biases and leaving under-represented communities out of the loop. Through a theoretical model of biased networks, we characterize the intricate relationship between diversity and efficiency, which sometimes may be at odds but may also reinforce each other. Most importantly, we find a mathematically proven analytical condition under which more equitable choices of early adopters lead simultaneously to fairer outcomes and larger outreach. Analysis of data on the DBLP network confirms that our condition is often met in real networks. We design and test a set of algorithms leveraging the network structure to optimize the diffusion of a message while avoiding to create disparate impact among participants based on their demographics, such as gender or race.

References

[1]
Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P Gummadi, and Adish Singla. 2019. On the Fairness of Time-Critical Influence Maximization in Social Networks. arXiv preprint arXiv:1905.06618(2019).
[2]
Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. Discrimination through optimization: How Facebook’s ad delivery can lead to skewed outcomes. arXiv preprint arXiv:1904.02095(2019).
[3]
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica (May 2016).
[4]
Chen Avin, Avi Cohen, Pierre Fraigniaud, Zvi Lotker, and David Peleg. 2018. Preferential attachment as a unique equilibrium. In Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 559–568.
[5]
Chen Avin, Barbara Keller, Zvi Lotker, Claire Mathieu, David Peleg, and Yvonne-Anne Pignolet. 2015. Homophily and the glass ceiling effect in social networks. In Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science. ACM, 41–50.
[6]
Eric Balkanski, Nicole Immorlica, and Yaron Singer. 2017. The Importance of Communities for Learning to Influence. In Advances in Neural Information Processing Systems. 5864–5873.
[7]
Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science 286, 5439 (1999), 509–512.
[8]
Solon Barocas and Moritz Hardt. 2017. Fairness in Machine Learning Tutorial. Neural Information Processing Systems(2017).
[9]
Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. 77–91.
[10]
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840(2017).
[11]
Wei Chen, Chi Wang, and Yajun Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1029–1038.
[12]
Wei Chen, Yajun Wang, and Siyu Yang. 2009. Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 199–208.
[13]
Sam Corbett-Davies and Sharad Goel. 2018. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023(2018).
[14]
David A Cotter, Joan M Hermsen, Seth Ovadia, and Reeve Vanneman. 2001. The glass ceiling effect. Social forces 80, 2 (2001), 655–681.
[15]
Benjamin Edelman, Michael Luca, and Dan Svirsky. 2017. Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics 9, 2 (2017), 1–22.
[16]
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 259–268.
[17]
Benjamin Fish, Ashkan Bashardoust, Danah Boyd, Sorelle Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019. Gaps in Information Access in Social Networks?. In The World Wide Web Conference. ACM, 480–490.
[18]
Anikó Hannák, Claudia Wagner, David Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017. Bias in online freelance marketplaces: Evidence from taskrabbit and fiverr. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 1914–1933.
[19]
Moritz Hardt, Eric Price, and Nathan Srebro. 2016. Equality of Opportunity in Supervised Learning. arXiv preprint arXiv:1610.02413(2016).
[20]
Catherine Hill, Christianne Corbett, and Andresse St Rose. 2010. Why so few? Women in science, technology, engineering, and mathematics.ERIC.
[21]
Lu Hong and Scott E Page. 2004. Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences 101, 46(2004), 16385–16389.
[22]
David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 137–146.
[23]
David Kempe, Jon Kleinberg, and Éva Tardos. 2005. Influential nodes in a diffusion model for social networks. In International Colloquium on Automata, Languages, and Programming. Springer, 1127–1138.
[24]
Masahiro Kimura and Kazumi Saito. 2006. Tractable models for information diffusion in social networks. In European conference on principles of data mining and knowledge discovery. Springer, 259–271.
[25]
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. 2018. Algorithmic fairness. In Aea papers and proceedings, Vol. 108. 22–27.
[26]
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807(2016).
[27]
Jon Kleinberg and Manish Raghavan. 2018. Selection Problems in the Presence of Implicit Bias. In Proc. 9th Conf. on Innovations in Theoretical Computer Science (ITCS).
[28]
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D Sivakumar, Andrew Tomkins, and Eli Upfal. 2000. Stochastic models for the web graph. In Proceedings 41st Annual Symposium on Foundations of Computer Science. IEEE, 57–65.
[29]
Eun Lee, Fariba Karimi, Claudia Wagner, Hang-Hyun Jo, Markus Strohmaier, and Mirta Galesic. 2019. Homophily and minority-group size explain perception biases in social networks. Nature human behaviour 3, 10 (2019), 1078–1087.
[30]
Michael Ley. 2009. DBLP: some lessons learned. Proceedings of the VLDB Endowment 2, 2 (2009), 1493–1500.
[31]
Alan Mislove, Sune Lehmann, Yong-Yeol Ahn, Jukka-Pekka Onnela, and J Niels Rosenquist. 2011. Understanding the demographics of twitter users. In Fifth international AAAI conference on weblogs and social media.
[32]
Corinne A Moss-Racusin, John F Dovidio, Victoria L Brescoll, Mark J Graham, and Jo Handelsman. 2012. Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences 109, 41(2012), 16474–16479.
[33]
Arvind Narayanan. 2018. Translation tutorial: 21 fairness definitions and their politics. In Proc. Conf. Fairness Accountability Transp., New York, USA.
[34]
Shirin Nilizadeh, Anne Groggel, Peter Lista, Srijita Das, Yong-Yeol Ahn, Apu Kapadia, and Fabio Rojas. 2016. Twitter’s Glass Ceiling: The Effect of Perceived Gender on Online Visibility. In Tenth International AAAI Conference on Web and Social Media.
[35]
Scott E Page. 2008. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies-New Edition. Princeton University Press.
[36]
Sharon Sassler, Yael Levitte, Jennifer Glass, and Katherine Michelmore. 2011. The missing women in stem? accounting for gender differences in entrance into stem occupations. In Annual meeting of the Population Association of America Presentation.
[37]
Lior Seeman and Yaron Singer. 2013. Adaptive seeding in social networks. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. IEEE, 459–468.
[38]
Camelia Simoiu, Sam Corbett-Davies, and Sharad Goel. 2017. The Problem of Infra-marginality in Outcome Tests for Discrimination. Annals of Applied Statistics 11 (2017).
[39]
Ana-Andreea Stoica and Augustin Chaintreau. 2019. Fairness in Social Influence Maximization. In Companion Proceedings of The 2019 World Wide Web Conference. 569–574.
[40]
Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau. 2018. Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity. In Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 923–932.
[41]
Randall Stross. 2008. What has driven women out of computer science. New York Times 15(2008).
[42]
Alan Tsang, Bryan Wilder, Eric Rice, Milind Tambe, and Yair Zick. 2019. Group-fairness in influence maximization. arXiv preprint arXiv:1903.00967(2019).
[43]
Sandra Wachter. 2019. Affinity Profiling and Discrimination by Association in Online Behavioural Advertising. Available at SSRN (2019).
[44]
Stanley Wasserman, Katherine Faust, 1994. Social network analysis: Methods and applications. Vol. 8. Cambridge university press.
[45]
Michele A Whitecraft and Wendy M Williams. 2010. Why aren’t more women in computer science. Making software: What really works, and why we believe it (2010), 221–238.
[46]
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1171–1180.
[47]
Honglei Zhuang, Yihan Sun, Jie Tang, Jialin Zhang, and Xiaoming Sun. 2013. Influence maximization in dynamic social networks. In 2013 IEEE 13th International Conference on Data Mining. IEEE, 1313–1318.

Cited By

View all
  • (2025)A homophilic and dynamic influence maximization strategy based on independent cascade model in social networksFrontiers in Physics10.3389/fphy.2024.150990512Online publication date: 3-Jan-2025
  • (2025)Efficient Continuous Network DismantlingIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.349669455:2(976-989)Online publication date: Feb-2025
  • (2024)Networked inequalityProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693978(46891-46925)Online publication date: 21-Jul-2024
  • Show More Cited By

Index Terms

  1. Seeding Network Influence in Biased Networks and the Benefits of Diversity
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WWW '20: Proceedings of The Web Conference 2020
    April 2020
    3143 pages
    ISBN:9781450370233
    DOI:10.1145/3366423
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 April 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. fairness
    2. graph algorithms
    3. influence
    4. social networks

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    WWW '20
    Sponsor:
    WWW '20: The Web Conference 2020
    April 20 - 24, 2020
    Taipei, Taiwan

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)83
    • Downloads (Last 6 weeks)11
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)A homophilic and dynamic influence maximization strategy based on independent cascade model in social networksFrontiers in Physics10.3389/fphy.2024.150990512Online publication date: 3-Jan-2025
    • (2025)Efficient Continuous Network DismantlingIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.349669455:2(976-989)Online publication date: Feb-2025
    • (2024)Networked inequalityProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693978(46891-46925)Online publication date: 21-Jul-2024
    • (2024)A General Concave Fairness Framework for Influence Maximization Based on Poverty RewardACM Transactions on Knowledge Discovery from Data10.1145/370173719:1(1-23)Online publication date: 28-Oct-2024
    • (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
    • (2024)FairSNA: Algorithmic Fairness in Social Network AnalysisACM Computing Surveys10.1145/365371156:8(1-45)Online publication date: 26-Apr-2024
    • (2023)Scalable fair influence maximizationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669033(66675-66691)Online publication date: 10-Dec-2023
    • (2023)Group fairness without demographics using social networksProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594091(1432-1449)Online publication date: 12-Jun-2023
    • (2023)Constrained Subset Selection from Data Streams for Profit MaximizationProceedings of the ACM Web Conference 202310.1145/3543507.3583490(1822-1831)Online publication date: 30-Apr-2023
    • (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
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media