Mehrdad Farajtabar

Ph.D. Student

Optimization and Intervention in Networks

  Halls department, Hall 5
  Wednesday, 27 December 2017
  12:00 - 13:00

Abstract

Event sequences are ubiquitous in areas such as e-commerce, social networks, and health informatics. For example, events in e-commerce are the times a customer purchases a product from an online vendor such as Amazon. In social networks, event sequences are the times a user signs on or generates posts, clicks, and likes. In health informatics, events can be the times when a patient exhibits symptoms or receives treatments. Temporal point process is an effective mathematical tool for modeling events data. It is a random process whose realization consists of a list of discrete events localized in time. In this talk, we introduce the point process framework and then exemplify an instance of it, COEVOLVE, for joint dynamics of information diffusion and network evolution, allowing the intensity of one process to be modulated by that of the other. Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn, these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. Our joint model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks.

Bio

Mehrdad Farajtabar is a Ph.D. student in Computational Science and Engineering at the College of Computing, Georgia Institute of Technology.
His research interests are machine learning and scalable data mining methods for the analysis and modeling of real-world networks and processes over them. He's thrilled to work with an outstanding group doing top-notch Machine Learning research under the supervision of Prof. Zha and Prof. Song.
Prior to joining Georgia Tech, He received his M.Sc. in Artificial Intelligence from Computer Engineering Department at the Sharif University of Technology under the supervision of Prof. Rabiee and his B.Sc. from the same university in Software Engineering.