Fast Algorithms for Mining Large Graphs

Date: 
Wednesday, October 5, 2011 - 2:19pm

UCSB COMPUTER SCIENCE DEPARTMENT PRESENTS:

Thursday, October 20, 2011
3:30 – 4:30 PM
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Xifeng Yan

SPEAKER: Hanghang Tong
IBM T. J. Watson Research Center

Title: Fast Algorithms for Mining Large Graphs

Abstract:

Graphs appear in a wide range of settings and have posed a wealth of
fascinating research problems, e.g., Given a social network, how to
measure the closeness between two persons? How to track it over time?
How to identify abnormal behaviors of computer networks? In the case of
virus attacks, which nodes are the best to immunize? In this talk, I
will first give an overview of my research work. Then, I will focus on
three case studies, including (1) fast proximity computation on graphs;
(2) interpretable matrix factorization tools to find graph patterns; and
(3) scalable immunization algorithms on real graphs. At the end of the
talk, I will also introduce people and network analytics at IBM research.

Bio:

Hanghang Tong is currently a research staff member at IBM T. J. Watson
Research Center. Before that, he was a Post-doctoral fellow in Carnegie
Mellon University. He received his M.Sc and Ph.D. degree from Carnegie
Mellon University in 2008 and 2010, both majored in Machine Learning.
His research interest is in large scale data mining for graphs and
multimedia. He was a co-PI in NSF sponsored project on virus and
influence propagation on large graphs. He is currently a co-PI in DARPA
sponsored project on anomaly detection as multi-scales (ADAMS), and an
IBM co-PI in the Social and Cognitive Networks Academic Research Center
(SCNARC) sponsored by Army Research Lab. His current task focuses on
composite networks in organization and team performance. He has received
several awards, including best research paper in ICDM 2006 and best
paper award in SDM 2008. He has published over 40 referred articles and
served as a program committee of SIGKDD, PKDD, and WWW.