Influence Maximization: Pushing the Limits of Combinatorial Optimizations and Online Learning (Wei Chen)


Influence maximization is the task of selecting a number of seed nodes in a social network to maximize its influence spread in the network, and it has many applications in viral marketing, social recommendations, rumor control, etc. Influence maximization and its variants provide a rich set of tasks that often push the limits of existing researches on optimization and learning. In this talk, I will use my recent research work on adaptive influence maximization and online influence maximization as examples to demonstrate how influence maximization pushes adaptive stochastic optimization and combinatorial online learning to the new grounds, and enriches our understanding in these areas along the way.


2019-12-11   09:00 ~ 10:00   


Wei Chen,  Microsoft Research Asia


First floor, New Lab building, Shanghai University of Finance & Economics