Join us on 11/29 at 2:30p in Room 120 of the New Computer Science building for the following faculty candidate talk:

Hongyuan Zha, Georgia Tech

Title: Optimization and intervention in point processes networks with application to activity shaping and social campaigning

Abstract:
Steering the activities on social networks to a desirable outcome is of many practical, economic, and societal interest. For example, can one model and exploit social network dynamic behaviors to steer the online community to a desired activity level? Specifically, can one drive the overall exposure to a campaign to a certain level (e.g., at least twice per day per user) by incentivizing a small number of users to take more initiatives? What about maximizing the overall service usage for a target group of users? How about making sure that a job opportunity news is shared uniformly and without discrimination in the social network? Furthermore, these activity shaping problems need to be addressed by taking into account budget constraints, since incentives are usually provided in the form of monetary rewards. We model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. Next, we consider the manipulation problem in a multi-stage adaptive setting where one is allowed to intervene in the middle of the process after observing the early outcomes. We formulate the problem as a Markov decision problem propose a convex dynamic programming framework to find the optimal policy that balances the high present reward and large penalty on low future outcome in the presence of extensive uncertainties. This talk will establish the fundamentals of intervention and control in networks by combining the rich area of temporal point processes and the well-developed framework of Markov decision processes.

http://www.cc.gatech.edu/~zha/