A new non-parametric feature learning for supervised link prediction

Abstract

Link prediction is an important task for analysing relational data such as the friendship relation on a social networking website that also has applications in other domains like, information retrieval, bioinformatics and e-commerce. The problem of link prediction is to predict the existence or absence of edges between vertices of a network. In this paper, we present a novel non-parametric latent feature relational model based on distance dependent Indian buffet process (DDIBP), by which we can utilise the information of topological structure of the network such as shortest path and connectivity of the nodes and incorporate them into the proposed Bayesian Non-parametric latent feature model which can automatically infer the unknown latent feature dimension. We also develop an efficient MCMC algorithm to compute the posterior distribution of the hidden variables with a highly nonlinear link likelihood function. Experimental results on four real datasets demonstrate the superiority of the proposed method over other latent feature models for link prediction problem.