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Pioneers of Influence Propagation in Social Networks

  • Kumar Gaurav
  • Bartłomiej Błaszczyszyn
  • Paul Holger Keeler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8591)

Abstract

In this paper, we present a diffusion model developed by enriching the generalized random graph (a.k.a. configuration model), motivated by the phenomenon of viral marketing in social networks. The main results on this model are rigorously proved in [3], and in this paper we focus on applications. Specifically, we consider random networks having Poisson and Power Law degree distributions where the nodes are assumed to have varying attitudes towards influence propagation, which we encode in the model by their transmitter degrees. We link a condition involving total degree and transmitter degree distributions to the effectiveness of a marketing campaign. This suggests a novel approach to decision-making by a firm in the context of viral marketing which does not depend on the detailed information of the network structure.

Keywords

Degree Distribution Online Social Network Marketing Campaign Social Graph Unique Zero 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kumar Gaurav
    • 1
  • Bartłomiej Błaszczyszyn
    • 2
  • Paul Holger Keeler
    • 2
  1. 1.UPMC/Inria/ENSParisFrance
  2. 2.Inria/ENSParisFrance

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