Algorithms for analyzing on-line social network data

Jan. 14, 2015 from 3:30 to 4:20 p.m. — Physics/Astronomy Auditorium, room A102

Professor, Cornell University


On-line social media systems are not simply venues for people to come together; they are also explicitly designed environments in which the underlying data is used to guide the behavior of the system and its participants. Here we consider several data challenges for on-line social systems that illustrate this tension between organic interaction and algorithmic design. In particular, we consider the problem of managing personal information streams by predicting which pieces of content will lead to the most active discussions and which will be shared the most broadly. We also study the underlying social network to identify links along which interaction is the strongest. Our analysis sheds light on the dynamics of large on-line discussions and the collective structure of people’s strongest social ties.


Professor Kleinberg is a professor at Cornell University. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. His work has been supported by an NSF Career Award, an ONR Young Investigator Award, a MacArthur Foundation Fellowship, a Packard Foundation Fellowship, a Simons Investigator Award, a Sloan Foundation Fellowship, and grants from Google, Yahoo!, and the NSF. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.