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RTIM: A Real-Time Influence Maximization Strategy

  • David Dupuis
  • Cédric du Mouza
  • Nicolas TraversEmail author
  • Gaël Chareyron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Influence Maximization (IM) consists in finding in a network the top-k influencers who will maximize the diffusion of information. However, the exponential growth of online advertisement is due to Real-Time Bidding (RTB) which targets users on webpages. It requires complex ad placement decisions in real-time to face a high-speed stream of users. In order to stay relevant, the IM problem should be updated to answer RTB needs. While traditional IM generates a static set of influencers, they do not fit with an RTB environment which requires dynamic influence targeting. This paper proposes RTIM, the first IM algorithm capable of targeting users in a RTB environment. We also analyze influence scores of users in several social networks and provide a thorough experimental process to compare static versus dynamic IM solutions.

Keywords

Real-Time Bidding Influence Maximization Social network 

Notes

Acknowledgments

This work was supported by Kwanko.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David Dupuis
    • 1
    • 2
  • Cédric du Mouza
    • 3
  • Nicolas Travers
    • 1
    • 3
    Email author
  • Gaël Chareyron
    • 1
  1. 1.Léonard de Vinci Pôle Universitaire, Research CenterParis La DéfenseFrance
  2. 2.KwankoParisFrance
  3. 3.CEDRIC Lab, CNAMParisFrance

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