IDEAS home Printed from https://ideas.repec.org/a/cup/netsci/v1y2013i02p125-153_00.html
   My bibliography  Save this article

Engineering social contagions: Optimal network seeding in the presence of homophily

Author

Listed:
  • ARAL, SINAN
  • MUCHNIK, LEV
  • SUNDARARAJAN, ARUN

Abstract

We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build, and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily. Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding). Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.

Suggested Citation

  • Aral, Sinan & Muchnik, Lev & Sundararajan, Arun, 2013. "Engineering social contagions: Optimal network seeding in the presence of homophily," Network Science, Cambridge University Press, vol. 1(2), pages 125-153, August.
    • Handle: RePEc:cup:netsci:v:1:y:2013:i:02:p:125-153_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S2050124213000064/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    2. Ilan Lobel & Evan Sadler & Lav R. Varshney, 2017. "Customer Referral Incentives and Social Media," Management Science, INFORMS, vol. 63(10), pages 3514-3529, October.
    3. Thomas Chesney, 2017. "The Cascade Capacity Predicts Individuals to Seed for Diffusion Through Social Networks," Systems Research and Behavioral Science, Wiley Blackwell, vol. 34(1), pages 51-61, January.
    4. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    5. Erbao Cao & He Li, 2020. "Group buying and consumer referral on a social network," Electronic Commerce Research, Springer, vol. 20(1), pages 21-52, March.
    6. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    7. Prasanta Bhattacharya & Tuan Q. Phan & Xue Bai & Edoardo M. Airoldi, 2019. "A Coevolution Model of Network Structure and User Behavior: The Case of Content Generation in Online Social Networks," Service Science, INFORMS, vol. 30(1), pages 117-132, March.
    8. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
    9. K. Sudhir & Seung Yoon Lee & Subroto Roy, 2021. "Lookalike Targeting on Others' Journeys: Brand Versus Performance Marketing," Cowles Foundation Discussion Papers 2302, Cowles Foundation for Research in Economics, Yale University.
    10. Kai Zhu & Dylan Walker & Lev Muchnik, 2020. "Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia," Information Systems Research, INFORMS, vol. 31(2), pages 491-509, June.
    11. Wang, Feng & Zhang, Xueting & Chen, Man & Zeng, Wei & Cao, Rong, 2022. "The influential paradox: Brand and deal content sharing by influencers in friendship networks," Journal of Business Research, Elsevier, vol. 150(C), pages 503-514.
    12. Vahideh Manshadi & Sidhant Misra & Scott Rodilitz, 2020. "Diffusion in Random Networks: Impact of Degree Distribution," Operations Research, INFORMS, vol. 68(6), pages 1722-1741, November.
    13. Alex Chin & Dean Eckles & Johan Ugander, 2022. "Evaluating Stochastic Seeding Strategies in Networks," Management Science, INFORMS, vol. 68(3), pages 1714-1736, March.
    14. Raghuram Iyengar & Christophe Van den Bulte & Jae Young Lee, 2015. "Social Contagion in New Product Trial and Repeat," Marketing Science, INFORMS, vol. 34(3), pages 408-429, May.
    15. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    16. Grewal, Dhruv & Bart, Yakov & Spann, Martin & Zubcsek, Peter Pal, 2016. "Mobile Advertising: A Framework and Research Agenda," Journal of Interactive Marketing, Elsevier, vol. 34(C), pages 3-14.
    17. Michael Olabisi, 2019. "Bridging the enforcement gap in international trade: Participation in the New York Convention on arbitration," Journal of International Business Policy, Palgrave Macmillan, vol. 2(1), pages 86-109, March.
    18. Rakesh R. Mallipeddi & Subodha Kumar & Chelliah Sriskandarajah & Yunxia Zhu, 2022. "A Framework for Analyzing Influencer Marketing in Social Networks: Selection and Scheduling of Influencers," Management Science, INFORMS, vol. 68(1), pages 75-104, January.
    19. Fu, Guiyuan & Chen, Feier & Liu, Jianguo & Han, Jingti, 2019. "Analysis of competitive information diffusion in a group-based population over social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 409-419.
    20. Nejad, Mohammad G. & Amini, Mehdi & Sherrell, Daniel L., 2016. "The profit impact of revenue heterogeneity and assortativity in the presence of negative word-of-mouth," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 656-673.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:netsci:v:1:y:2013:i:02:p:125-153_00. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/nws .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.