Hanghang Tong
Assistant professor at Computer Science Department, City College, City University of New York
时间：2014 年 7 月 9 日上午 9:00-10:30
地点：张江校区软件楼 102 第二会议室
承办单位：复旦大学计算机科学技术学院
联系人：肖仰华 (shawyh@fudan.edu.cn)
Abstract:
Big graphs are prevalent and are becoming a popular platform for the dissemination of a variety of information (e.g., viruses, memes, opinions, rumors, etc). In this talk, we focus on the problem of optimally affecting the outcome of dissemination by manipulating the underlying graph structure. We aim to answer two questions: (1) what are the key graph parameters for the so-called tipping point? and (2) how can we design effective algorithms to optimize such parameters in a desired way? We show that for a large family of dissemination models, the problem becomes optimizing the leading eigen-value of an appropriately defined system matrix associated with the underlying graph. We then present two algorithms as the instantiations of such an optimization problem - one to minimize the leading eigen-value (e.g., stopping virus propagation) and the other to maximize the eigen-value (e.g., promoting product adoption). If time allowed, I will also introduce our other work on analyzing big graphs.
Bio:
Hanghang Tong is currently an assistant professor at Computer Science Department, City College, City University of New York. Before that, he was a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degree from Carnegie Mellon University in 2008 and 2009, both majored in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. His research has been funded by NSF, DARPA and ARL. He has received several awards, including best paper award in CIKM 2012, best paper award in SDM 2008 and best research paper award in ICDM 2006. He has published over 80 referred articles and more than 20 patents. He has served as a program committee member in top data mining, databases and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc).
