Computational models for commercial advertisements in social networks

Samet Atdag and Haluk O. Bingol

Physica A: Statistical Mechanics and its Applications, 2021, vol. 572, issue C

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.

Keywords: Influencer detection; Computational advertisement models; Social networks; Social network economics; k-shell decomposition; Epidemics; Spreading ideas (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:572:y:2021:i:c:s0378437121001886

DOI: 10.1016/j.physa.2021.125916

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