Location

E14-493

Description

Social networks in many on-line applications encode a mixture of positive (friendly) and negative (antagonistic) relationships, but the bulk of research on these networks has focused almost exclusively on the positive interpretations of the links. Jure Leskovec (Stanford University) will present work on how the combination of positive and negative relationships affects the overall functioning of on-line social networks, and the interactions of the users who constitute them. Leskovec and his colleagues analyze the interplay of positive and negative relationships in these networks using classical theories of structural balance from social psychology. They find that these existing theories fail to explain many of the fundamental phenomena observed—particularly related to the evolving, directed nature of the networks—and they develop an alternate theory of status that provides insights into the mechanisms underlying these phenomena. Moreover, they find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites.
Joint work with Daniel Huttenlocher and Jon Kleinberg.
Links to papers:
http://cs.stanford.edu/people/jure/pubs/triads-chi10.pdf
http://cs.stanford.edu/people/jure/pubs/signs-www10.pdf

Biographies

Jure Leskovec is an assistant professor of computer science at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web, and on-line media. He received of three best paper awards and a ACM KDD dissertation award, won the ACM KDD Cup (2003), and topped the Battle of the Sensor Networks competition (2007). Jure also holds three patents and co-chairs the Machine Learning and Data Mining track at the upcoming World Wide Web conference.

Host/Chair: Human Dynamics