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RTIM: a Real-Time Influence Maximization Strategy

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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 influ-encers, 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.
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Dates and versions

hal-02465784 , version 1 (04-02-2020)

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David Dupuis, Cédric Du Mouza, Nicolas Travers, Gaël Chareyron. RTIM: a Real-Time Influence Maximization Strategy. Web Information Systems Engineering – WISE 2019, Nov 2019, Hong-Kong, China. ⟨10.1007/978-3-030-34223-4_18⟩. ⟨hal-02465784⟩
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