The Social Medium Selection Game

  • Fabrice Lebeau
  • Corinne TouatiEmail author
  • Eitan Altman
  • Nof Abuzainab
Conference paper
Part of the Static & Dynamic Game Theory: Foundations & Applications book series (SDGTFA)


We consider in this paper competition of content creators in routing their content through various media. The routing decisions may correspond to the selection of a social network (e.g., Twitter versus Facebook or Linkedin) or of a group within a given social network. The utility for a player to send its content to some medium is given as the difference between the dissemination utility at this medium and some transmission cost. We model this game as a congestion game and compute the pure potential of the game. In contrast to the continuous case, we show that there may be various equilibria. We show that the potential is M-concave which allows us to characterize the equilibria and to propose an algorithm for computing it. We then give a learning mechanism which allow us to give an efficient algorithm to determine an equilibrium. We finally determine the asymptotic form of the equilibrium and discuss the implications on the social medium selection problem.


  1. 1.
    A. May, A. Chaintreau, N. Korula, and S. Lattanzi, “Game in the newsroom: Greedy bloggers for picky audience,” in Proc. of the 20th International Conference Companion on World Wide Web, February 2013, pp. 16–20.Google Scholar
  2. 2.
    E. Altman, “A semi-dynamic model for competition over popularity and over advertisement space in social networks,” in 6th International Conference on Performance Evaluation Methodologies and Tools, Oct. 2012, pp. 273–279.Google Scholar
  3. 3.
    A. Reiffers Masson, E. Altman, and Y. Hayel, “A time and space routing game model applied to visibility competition on online social networks,” in Proc. of the International Conference on Network Games, Control and Optimization, 2014.Google Scholar
  4. 4.
    N. Hegde, L. Massoulié, and L. Viennot, “Self-organizing flows in social networks,” in Structural Information and Communication Complexity, ser. Lecture Notes in Computer Science.   Springer International Publishing, 2013, vol. 8179, pp. 116–128.Google Scholar
  5. 5.
    Z. Lotker, B. Patt-Shamir, and M. R. Tuttle, “A game of timing and visibility,” Games and Economic Behavior, vol. 62, no. 2, pp. 643 – 660, 2008.MathSciNetCrossRefGoogle Scholar
  6. 6.
    L. Maggi and F. De Pellegrini, “Cooperative online native advertisement: A game theoretical scheme leveraging on popularity dynamics,” in Proc. of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2014, pp. 334–339.Google Scholar
  7. 7.
    N. Yadati and R. Narayanam, “Game theoretic models for social network analysis,” in Proc. of the 20th International Conference Companion on World Wide Web, 2011, pp. 291–292.Google Scholar
  8. 8.
    R. Narayanam and Y. Narahari, “A game theory inspired, decentralized, local information based algorithm for community detection in social graphs,” in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 1072–1075.Google Scholar
  9. 9.
    ——, “A Shapley value-based approach to discover influential nodes in social networks,” Automation Science and Engineering, IEEE Transactions on, vol. 8, no. 1, pp. 130–147, 2011.Google Scholar
  10. 10.
    R. W. Rosenthal, “A class of games possessing pure-strategy Nash equilibria,” International Journal of Game Theory, vol. 2, no. 1, 1973.MathSciNetCrossRefGoogle Scholar
  11. 11.
    K. Murota, “Discrete convex analysis,” Mathematical Programming, vol. 83, pp. 313–371, 1998.MathSciNetzbMATHGoogle Scholar
  12. 12.
    R. Ma, D. Chiu, J. Lui, and V. Misra, “On resource management for cloud users: A generalized Kelly mechanism approach,” Technical Report, CS, Columnia Univ, NY, 2010.Google Scholar
  13. 13.
    G. Tullock, “Efficient rent-seeking,” in Efficient Rent Seeking, 2001, pp. 3–16.Google Scholar
  14. 14.
    D. Monderer and L. S. Shapley, “Potential Games,” Games and Economic Behavior, vol. 14, 1996.MathSciNetCrossRefGoogle Scholar
  15. 15.
    S. Muriguchi, K. Murota, and A. Shioura, “Scaling Algorithms for M-convex Function Minimization,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E85-A, pp. 922–929, 2002.Google Scholar
  16. 16.
    I. Milchtaich, “Congestion games with player-specic payoff functions,” Games and Economic Behavior, vol. 13, pp. 111–124, 1996.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fabrice Lebeau
    • 1
  • Corinne Touati
    • 1
    Email author
  • Eitan Altman
    • 1
  • Nof Abuzainab
    • 2
  1. 1.InriaLe ChesnayFrance
  2. 2.Department of Electrical and Computer EngineeringVirginia TechBlacksburgUSA

Personalised recommendations