De-layering social networks by shared tastes of friendships

Abstract

Traditionally, social network analyses are applied to data from a particular social domain. With the advent of online social networks such as Facebook, we observe an aggregate of various social domains resulting in a layered mix of professional contacts, family ties, and different circles. These aggregates dilute the community structure. We provide a method for delayering social networks according to shared interests. Instead of relying on changes in the edge density, our shared taste model uses content of users to disambiguate the underlying shared interest of each friendship. We successfully de-layer real world networks from Library Thing and Boards. ie, obtaining topics that significantly outperform LDA on unsupervised prediction of group membership Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.