IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v70y2019i8p1224-1233.html
   My bibliography  Save this article

How to maximize advertising performance in online social networks

Author

Listed:
  • Yezheng Liu
  • Lingfei Li

Abstract

The influence maximization problem is widely studied, but previous studies have assumed that the cost to activate each seed node was identical. In this paper, we consider different activation costs and investigate a new problem: the budget-aware influence maximization problem (BIM). This problem is NP-hard, which motivates our interest in its approximation. We develop two greedy algorithms for BIM, namely, BG and GMUI, and show that GMUI obtains a solution that is provably (σ(Sk)−k)≥(1−1/e)(σ(OPT)−ω)−(k−1). Then, we consider the average profit to activate a common user and introduce GMUN, an improved GMUI algorithm. Finally, we evaluate our algorithms with experiments using two large real social networks. The results show that GMUI performs best in terms of influence, whereas GMUN creates leverage between the budget and net income, indicating that the marginal net income/costs ratio for each seed node selected by GMUN can satisfy enterprises.

Suggested Citation

  • Yezheng Liu & Lingfei Li, 2019. "How to maximize advertising performance in online social networks," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1224-1233, August.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:8:p:1224-1233
    DOI: 10.1080/01605682.2018.1489343
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2018.1489343
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01605682.2018.1489343?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.

    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:taf:tjorxx:v:70:y:2019:i:8:p:1224-1233. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.