ChoiceGAPs: Competitive Diffusion as a Massive Multi-player Game in Social Networks

  • Edoardo Serra
  • Francesca Spezzano
  • V. S. Subrahmanian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9858)


We consider the problem of modeling competitive diffusion in real world social networks via the notion of ChoiceGAPs which combine choice logic programs and Generalized Annotated Programs. We assume that each vertex in a social network is a player in a multi-player game (with a huge number of players) — the choice part of the ChoiceGAPs describes utilities of players for acting in various ways based on utilities of their neighbors in those and other situations. We define multi-player Nash equilibrium for such programs — but because they require some conditions that are hard to satisfy in the real world, we introduce the new model-theoretic concept of strong equilibrium. We show that strong equilibria can capture all Nash equilibria. We prove a host of complexity (intractability) results for checking existence of strong equilibria and identify a class of ChoiceGAPs for which strong equilibria can be polynomially computed. We perform experiments on a real-world Facebook data set surrounding the 2013 Italian election and show that our algorithms have good predictive accuracy with an Area Under a ROC Curve that, on average, is over 0.76.


Social Network Nash Equilibrium Receiver Operating Characteristic Curve Predicate Symbol Ground Atom 
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.



Parts of this work were supported by ARO grant W911NF1610342.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Edoardo Serra
    • 1
  • Francesca Spezzano
    • 1
  • V. S. Subrahmanian
    • 2
  1. 1.Computer Science DepartmentBoise State UniversityBoiseUSA
  2. 2.Computer Science DepartmentUniversity of MarylandCollege ParkUSA

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