Social media websites promote diverse user interaction on media objects as well as user actions with respect to other users. The goal of this work is to discover community structu...
Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi B. Konuru...
Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of resea...
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to c...
Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovi...
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...