Social network popularity continues to rise as they broaden out to more users. Hidden away within these social networks is a valuable set of data that outlines everyone’s relatio...
Thomas Tran, Kelcey Chan, Shaozhi Ye, Prantik Bhat...
This paper introduces a new discriminative learning technique for link prediction based on the matrix alignment approach. Our algorithm automatically determines the most predictiv...
Jerry Scripps, Pang-Ning Tan, Feilong Chen, Abdol-...
In our work, we address the problem of modeling social network generation which explains both link and group formation. Recent studies on social network evolution propose generati...
In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for dir...
This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse network...
Ryan Lichtenwalter, Jake T. Lussier, Nitesh V. Cha...