Influence Maximization for Informed Agents in Collective Behavior

Control of collective behavior is an active topic in biology, social, and computer science. In this work we investigate how a minority of informed agents can influence and control the whole society through local interactions. The problem we specifically target is that a minority of people with a bounded budget for initiating new social relations attempt to control the collective behavior of a society and move the crowd toward a specific goal. Assuming that local interactions can only take place between friends, the minority has to initiate some new relations with the majority. The total cost of new relations is limited to a budget. The problem is then finding the optimal links in order to gain maximum impact on the society. We will model the problem as a diffusion process in a social network. The proof of NP-hardness of the problem for Local Interaction Game model of diffusion is presented. Simulations show that the proposed method surpasses the popular strategies based on degree and distance centrality in performance.

[1]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

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

[6]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[7]  Andreas Krause,et al.  Near-optimal Observation Selection using Submodular Functions , 2007, AAAI.

[8]  Carlos Guestrin,et al.  A Note on the Budgeted Maximization of Submodular Functions , 2005 .

[9]  T. Valente,et al.  Accelerating the Diffusion of Innovations Using Opinion Leaders , 1999 .

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

[11]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[12]  Samir Khuller,et al.  The Budgeted Maximum Coverage Problem , 1999, Inf. Process. Lett..

[13]  Eyal Even-Dar,et al.  A note on maximizing the spread of influence in social networks , 2007, Inf. Process. Lett..

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

[15]  Jaideep Srivastava,et al.  A Generalized Linear Threshold Model for Multiple Cascades , 2010, 2010 IEEE International Conference on Data Mining.

[16]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[17]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

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

[19]  R. Durrett.  Lecture notes on particle systems and percolation , 1988 .

[20]  P. Lazarsfeld,et al.  The People's Choice: How the Voter Makes Up His Mind in a Presidential Campaign , 1968 .

[21]  Pawel Sobkowicz,et al.  Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality , 2009, J. Artif. Soc. Soc. Simul..

[22]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

[23]  Roland Siegwart,et al.  Building Mixed Societies of Animals and Robots , 2005 .

[24]  B. Bollobás.  The evolution of random graphs , 1984 .

[25]  Roland Siegwart,et al.  Animal and robot mixed societies: building cooperation between microrobots and cockroaches , 2005, IEEE Robotics & Automation Magazine.