Identifying Influential Groups in Networked Systems.


Verified data and predictions show that traditional forms of advertising, such as billboards, newspapers, radio, and magazines, are increasingly being replaced by online advertisements. This in effect allows targeted advertising, which in general means that corporations (e.g., tech companies and political campaigns) could potentially direct their advertising budgets toward most receptive consumers. One effective method of targeted advertising is to target a group of consumers who, once convinced by the quality of the product, are more likely to help spread positive information on the product throughout the network. This leads to the fundamental problem of identifying influential groups in a social network, which constitutes the focus of this talk. It is assumed throughout that an individual’s opinion is a scalar (or a real-valued vector) and the dynamics of the network is given. I will first propose original methods aiming to steer the entire network toward an agreement on an arbitrary value, by controlling a minimal number of individuals. I will address a more general problem next, which is to shape the most extreme opinions existing in a social network in the long run, again through manipulating the opinions of a limited group of individuals. These findings open up a line of research that employs the dynamics of a social network, in addition to its structure, to redefine centrality indices of different groups of individuals within the network.


Type of Seminar:
 Control Seminar



Dr. Sadegh Bolouki


  Sunday 13 Esfand 96/4:30pm


 Bargh 4

Contact Person:

  Amin Rezaeizadeh

Biographical Sketch:

 Dr. Sadegh Bolouki is a Research Assistant Professor in the Department of Electrical and Computer Engineering at the State University of New York (SUNY) at Binghamton. Prior to joining SUNY, he was a Postdoctoral Research Scholar at the University of Illinois at Urbana-Champaign, working with Profs. Tamer Basar and Angelia Nedi?. Dr. Bolouki received the Ph.D. and B.S. degrees in Electrical Engineering from École Polytechnique de Montréal and Sharif University of Technology, respectively. His research interests broadly include distributed systems, complex networks, networked cyber-physical systems, reinforcement learning, and game theory.