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Computational models for commercial advertisements in social networks

Author

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  • Atdag, Samet
  • Bingol, Haluk O.

Abstract

Identifying noteworthy spreaders in a network is essential for understanding the spreading process and controlling the reach of the spread in the network. The nodes that are holding more intrinsic power to extend the reach of the spread are important due to demand for various applications such as viral marketing, controlling rumor spreading or getting a better understanding of spreading of the diseases. As an application of viral marketing, maximization of the reach with a fixed budget is a fundamental requirement in the advertising business. Distributing a fixed number of promotional items for maximizing the viral reach can leverage influencer detection methods. For detecting such “influencer” nodes, there are local metrics such as degree centrality (mostly used as in-degree centrality) or global metrics such as k-shell decomposition or eigenvector centrality. All the methods can rank graphs but they all have limitations and there is still no de-facto method for influencer detection in the domain.

Suggested Citation

  • Atdag, Samet & Bingol, Haluk O., 2021. "Computational models for commercial advertisements in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
  • Handle: RePEc:eee:phsmap:v:572:y:2021:i:c:s0378437121001886
    DOI: 10.1016/j.physa.2021.125916
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    References listed on IDEAS

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    1. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    2. Zhaoyi Li & Fei Xiong & Ximeng Wang & Hongshu Chen & Xi Xiong, 2019. "Topological Influence-Aware Recommendation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-12, February.
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    Cited by:

    1. Meliksah Turker & Haluk O. Bingol, 2023. "Multi-layer network approach in modeling epidemics in an urban town," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-13, February.

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