Publication Date:
2012-09-22
Description:
A recent surge of participatory web and social media has created a new laboratory for studying human relations and collective behavior on an unprecedented scale. In this work, we study the predictive power of social connections to determine the preferences or behaviors of individuals such as whether a user supports a certain political view, whether one likes a product, whether she would like to vote for a presidential candidate, etc. Since an actor is likely to participate in multiple different communities with each regulating the actor’s behavior in varying degrees, and a natural hierarchy might exist between these communities, we propose to zoom into a network at multiple different resolutions and determine which communities reflect a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on social media networks demonstrate the promising potential of the proposed approach in real-world applications. Content Type Journal Article Category Short Paper Pages 1-19 DOI 10.1007/s10115-012-0555-0 Authors Xufei Wang, Computer Science and Engineering, Arizona State University, Tempe, AZ, USA Lei Tang, Advertising Sciences, Walmart Labs, Santa Clara, CA, USA Huan Liu, Computer Science and Engineering, Arizona State University, Tempe, AZ, USA Lei Wang, School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia Journal Knowledge and Information Systems Online ISSN 0219-3116 Print ISSN 0219-1377
Print ISSN:
0219-1377
Electronic ISSN:
0219-3116
Topics:
Computer Science
