Gaussian Process Models for Link Analysis and Transfer Learning

Abstract

In this paper we develop a Gaussian process (GP) framework to model a collection of reciprocal random variables defined on the edges of a network. We show how to construct GP priors, i.e.,~covariance functions, on the edges of directed, undirected, and bipartite graphs. The model suggests an intimate connection between link prediction and transfer learning, which were traditionally considered two separate research topics. Though a straightforward GP inference has a very high complexity, we develop an efficient learning algorithm that can handle a large number of observations. The experimental results on several real-world data sets verify superior learning capacity.

Tasks

Reproductions