Security Games on Social Networks

Many real-world problems exhibit competitive situations in which a defender (a defending agent, agency, or organization) has to address misinformation spread by its adversary, e.g., health organizations cope with vaccination-related misinformation provided by anti-vaccination groups. The rise of social networks has allowed misinformation to be easily and quickly diffused to a large community. Taking into account knowledge of its adversary’s actions, the defender has to seek efficient strategies to limit the influence of the spread of misinformation by the opponent. In this paper, we address this problem as a blocking influence maximization problem using a game-theoretic approach. Two players strategically select a number of seed nodes in the social network that could initiate their own influence propagation. While the adversary attempts to maximize its negative influence, the defender tries to minimize this influence. We represent the problem as a zero-sum game and apply the Double Oracle algorithm to solve the game in combination with various heuristics for oracle phases. Our experimental results reveal that by using the game theoretic approach, we are able to significantly reduce the negative influence in comparison to when the defender does not do anything. In addition, we propose using an approximation of the payoff matrix, making the algorithms scalable to large real-world networks.

[1]  Robert L. Berg,et al.  American Journal of Preventive Medicine , 1986, The American Journal of Medicine.

[2]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[3]  J. A. Stockman Human Papillomavirus Vaccination Coverage on YouTube , 2010 .

[4]  Daniel I. S. Rosenbloom,et al.  Imitation dynamics of vaccination behaviour on social networks , 2011, Proceedings of the Royal Society B: Biological Sciences.

[5]  S. Chapman,et al.  Antivaccination activists on the world wide web , 2002, Archives of disease in childhood.

[6]  Tonya Oaks Smith A Little Birdie Told Me: H1N1 Information and Misinformation Exchange on Twitter , 2010 .

[7]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[8]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

[9]  Avrim Blum,et al.  Planning in the Presence of Cost Functions Controlled by an Adversary , 2003, ICML.

[10]  Milind Tambe,et al.  Security Games for Controlling Contagion , 2012, AAAI.

[11]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[12]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

[13]  Allan Borodin,et al.  Threshold Models for Competitive Influence in Social Networks , 2010, WINE.

[14]  A. Kata Anti-vaccine activists, Web 2.0, and the postmodern paradigm--an overview of tactics and tropes used online by the anti-vaccination movement. , 2012, Vaccine.

[15]  K. Wilson,et al.  An analysis of the Human Papilloma Virus vaccine debate on MySpace blogs. , 2010, Vaccine.

[16]  Divyakant Agrawal,et al.  Limiting the spread of misinformation in social networks , 2011, WWW.

[17]  Wei Chen,et al.  Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model , 2011, SDM.

[18]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[19]  Priya Shetty,et al.  Experts concerned about vaccination backlash , 2010, The Lancet.

[20]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[21]  K. Pauwels,et al.  Effects of Word-of-Mouth versus Traditional Marketing: Findings from an Internet Social Networking Site , 2009 .

[22]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[23]  Vincent Conitzer,et al.  A double oracle algorithm for zero-sum security games on graphs , 2011, AAMAS.