IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v33y2016i3p578-599.html
   My bibliography  Save this article

Sampling designs for recovering local and global characteristics of social networks

Author

Listed:
  • Ebbes, Peter
  • Huang, Zan
  • Rangaswamy, Arvind

Abstract

The trajectories of social processes (e.g., peer pressure, imitation, and assimilation) that take place on social networks depend on the structure of those networks. Thus, to understand a social process or to predict the associated outcomes accurately, marketers would need good knowledge of the social network structure. However, many social networks of relevance to marketers are large, complex, or hidden, making it prohibitively expensive to map out an entire social network. Instead, marketers often need to work with a sample (i.e., a subgraph) of a social network. In this paper we evaluate the efficacy of nine different sampling methods for generating subgraphs that recover four structural characteristics of importance to marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, which are important for understanding how social network structure influences outcomes of processes that take place on the network.

Suggested Citation

  • Ebbes, Peter & Huang, Zan & Rangaswamy, Arvind, 2016. "Sampling designs for recovering local and global characteristics of social networks," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 578-599.
    • Handle: RePEc:eee:ijrema:v:33:y:2016:i:3:p:578-599
    DOI: 10.1016/j.ijresmar.2015.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811615001214
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2015.09.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yaniv Dover & Jacob Goldenberg & Daniel Shapira, 2012. "Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data," Marketing Science, INFORMS, vol. 31(4), pages 689-712, July.
    2. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    3. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    4. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Peres, Renana, 2014. "The impact of network characteristics on the diffusion of innovations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 330-343.
    6. Valente, Thomas W. & Watkins, Susan C. & Jato, Miriam N. & Van Der Straten, Ariane & Tsitsol, Louis-Philippe M., 1997. "Social network associations with contraceptive use among Cameroonian women in voluntary associations," Social Science & Medicine, Elsevier, vol. 45(5), pages 677-687, September.
    7. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    8. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    9. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    10. Sheen S. Levine & Robert Kurzban, 2006. "Explaining clustering in social networks: towards an evolutionary theory of cascading benefits," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 27(2-3), pages 173-187.
    11. Herbert C. Kelman, 1958. "Compliance, identification, and internalization three processes of attitude change," Journal of Conflict Resolution, Peace Science Society (International), vol. 2(1), pages 51-60, March.
    12. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Stolz, Simon & Schlereth, Christian, 2021. "Predicting Tie Strength with Ego Network Structures," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 40-52.
    2. Yan Chen & Youran Qi & Qing Liu & Peter Chien, 2018. "Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 409-440, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Samadi, Mohammadreza & Nikolaev, Alexander & Nagi, Rakesh, 2016. "A subjective evidence model for influence maximization in social networks," Omega, Elsevier, vol. 59(PB), pages 263-278.
    4. Jansen, Nora & Hinz, Oliver, 2022. "Inferring opinion leadership from digital footprints," Journal of Business Research, Elsevier, vol. 139(C), pages 1123-1137.
    5. 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.
    6. Moldovan, Sarit & Steinhart, Yael & Lehmann, Donald R., 2019. "Propagators, Creativity, and Informativeness: What Helps Ads Go Viral," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 102-114.
    7. Yansong Hu & Christophe Van den Bulte, 2014. "Nonmonotonic Status Effects in New Product Adoption," Marketing Science, INFORMS, vol. 33(4), pages 509-533, July.
    8. Anja Lambrecht & Catherine Tucker & Caroline Wiertz, 2018. "Advertising to Early Trend Propagators: Evidence from Twitter," Marketing Science, INFORMS, vol. 37(2), pages 177-199, March.
    9. Tianshu Sun & Sean J. Taylor, 2020. "Displaying things in common to encourage friendship formation: A large randomized field experiment," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 237-271, September.
    10. Meyners, Jannik & Barrot, Christian & Becker, Jan U. & Bodapati, Anand V., 2017. "Reward-scrounging in customer referral programs," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 382-398.
    11. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    12. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.
    13. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.
    14. Pescher, Christian & Spann, Martin, 2014. "Relevance of actors in bridging positions for product-related information diffusion," Journal of Business Research, Elsevier, vol. 67(8), pages 1630-1637.
    15. Alex Chin & Dean Eckles & Johan Ugander, 2022. "Evaluating Stochastic Seeding Strategies in Networks," Management Science, INFORMS, vol. 68(3), pages 1714-1736, March.
    16. Mauricio Herrera & Guillermo Armelini & Erica Salvaj, 2015. "Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-25, October.
    17. Jing Wang & Anocha Aribarg & Yves F. Atchadé, 2013. "Modeling Choice Interdependence in a Social Network," Marketing Science, INFORMS, vol. 32(6), pages 977-997, November.
    18. Jurui Zhang & Raymond Liu, 2017. "Popularity of digital products in online social tagging systems," Journal of Brand Management, Palgrave Macmillan, vol. 24(1), pages 105-127, January.
    19. Radu Tanase & Claudio J Tessone & René Algesheimer, 2018. "Identification of influencers through the wisdom of crowds," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    20. Miriam Däs & Julia Klier & Mathias Klier & Georg Lindner & Lea Thiel, 2017. "Customer lifetime network value: customer valuation in the context of network effects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 307-328, November.

    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:eee:ijrema:v:33:y:2016:i:3:p:578-599. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    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.