ChoiceGAPs: Competitive Diffusion as a Massive Multi-player Game in Social Networks
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.
KeywordsSocial Network Nash Equilibrium Receiver Operating Characteristic Curve Predicate Symbol Ground Atom
Parts of this work were supported by ARO grant W911NF1610342.
- 1.Online appendix (2016). https://sites.google.com/site/choicegap
- 3.Apt, K.R., Simon, S.: Social network games with obligatory product selection. In: GandALF, pp. 180–193 (2013)Google Scholar
- 6.Broecheler, M., Shakarian, P., Subrahmanian, V.S.: A scalable framework for modeling competitive diffusion in social networks. In: SocialCom/PASSAT, pp. 295–302 (2010)Google Scholar
- 7.Carnes, T., Nagarajan, C., Wild, S.M., van Zuylen, A.: Maximizing influence in a competitive social network: a follower’s perspective. In: ICEC 2007, pp. 351–360 (2007)Google Scholar
- 8.Cha, M., Mislove, A., Gummadi, P.K.: A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the International World Wide Web Conference, pp. 721–730 (2009)Google Scholar
- 10.He, X., Song, G., Chen, W., Jiang, Q.: Influence blocking maximization in social networks under the competitive linear threshold model. In: SDM, p. 463 (2012)Google Scholar
- 12.Jackson, M., Yariv, L.: Diffusion on social networks. Economie Publique 16, 69–82 (2005)Google Scholar
- 13.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)Google Scholar
- 17.Saccà, D., Zaniolo, C.: Stable models and non-determinism in logic programs with negation. In: PODS, pp. 205–217 (1990)Google Scholar
- 18.Schelling, T.C.: Micromotives and Macrobehavior. W.W. Norton and Co., New York (1978)Google Scholar
- 19.Shakarian, P., Broecheler, M., Subrahmanian, V.S., Molinaro, C.: Using generalized annotated programs to solve social network diffusion optimization problems. In: ACM Transactions on Computational Logic (2012)Google Scholar