HotCSE Seminar
Computational Science & Engineering
Wednesday September 09, 12pm-1pm, 1116-E Klaus

Shaping Social Activity by Incentivizing Users

Mehrdad Farajtabar
Advisor: Prof. Hongyuan Zha and Le Song


Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this work, 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. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.


Mehrdad Farajtabar is a third-year Ph.D. student in CSE under supervision of Hongyuan Zha and Le Song. His research interests are machine learning and scalable data mining methods for the analysis and modeling of real world networks and processes over them.