Computer Sciences Colloquium: RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks

Abstract:

Users in social networks whose posts stay at the top of their followers' feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple -- it is given by the position of her most recent post on each of her follower's feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post. Experiments on both synthetic and real data gathered from Twitter show that our algorithm is able to consistently make a user's posts more visible over time, is robust to volume changes on her followers' feeds, and significantlyoutperforms the state of the art.

Bio: Manuel Gomez Rodriguez is a tenure-track faculty at Max Planck Institute for Software Systems. 

Manuel develops machine learning and large-scale data mining methods for the analysis, modeling and control of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including an Outstanding Paper Award at NIPS'13 and a Best Research Paper Honorable Mention at KDD'10. Manuel holds a PhD in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find more about him at http://www.mpi-sws.org/~manuelgr/.