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Viral marketing on social networks: An epidemiological perspective

Author

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  • Bhattacharya, Saumik
  • Gaurav, Kumar
  • Ghosh, Sayantari

Abstract

Omnipresent online social media nowadays has a constantly growing influence on business, politics, and society. Understanding these newer mechanisms of information diffusion is very important for deciding campaign policies. Due to free interaction among a large number of members, information diffusion on social media has various characteristics similar to an epidemic. In this paper, we propose and analyze a mathematical model to understand the phenomena of digital marketing with an epidemiological approach considering some realistic interactions in a social network. We apply mean-field approach as well as network analysis to investigate the phenomenon for both homogeneous and heterogeneous models, and study the diffusion dynamics as well as equilibrium states for both the cases. We explore the parameter space and design strategies to run an advertisement campaign with substantial efficiency. Moreover, we observe the phenomena of bistability, following which we estimate the necessary conditions to make a campaign more sustainable while ensuring its viral spread.

Suggested Citation

  • Bhattacharya, Saumik & Gaurav, Kumar & Ghosh, Sayantari, 2019. "Viral marketing on social networks: An epidemiological perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 478-490.
    • Handle: RePEc:eee:phsmap:v:525:y:2019:i:c:p:478-490
    DOI: 10.1016/j.physa.2019.03.008
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    References listed on IDEAS

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    Cited by:

    1. Gaurav, Kumar & Ghosh, Sayantari & Bhattacharya, Saumik & Singh, Yatindra Nath, 2019. "Ensuring the Spread of Referral Marketing Campaigns: A Quantitative Treatment," SocArXiv 6spnr, Center for Open Science.
    2. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    3. Devi, Kalyanee & Tripathi, Rohit, 2023. "ASN: A method of optimality for seed identification in the influence diffusion process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Deborah Lacitignola, 2021. "Handling Hysteresis in a Referral Marketing Campaign with Self-Information. Hints from Epidemics," Mathematics, MDPI, vol. 9(6), pages 1-17, March.

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