Link Prediction in Heterogeneous Networks Based on Tensor Factorization

Abstract

Link Prediction, that is, predicting the formation of links or interactions in a network in the future, is an important task in network analysis. Link prediction provides useful insights for other applications, such as recommendation system, disease-gene candidate detection and so on. Most link prediction methods assume that there is only one single type in the network. However, many real-world networks have heterogeneous interactions. Link prediction in such networks is challenging since (a) the network has a complicated dependency structure; and (b) the links of different types may carry different kinds of semantic meanings, which is important to distinguish the formation mechanisms of each link type. In this paper, we address these challenges by proposing a general method based on tensor factorization for link prediction in heterogeneous networks. Using a CANDECOMP/PARAFAC tensor factorization of the data, we illustrate the usefulness of exploring the natural three-dimensional structure of heterogeneous network. The experiment on real-world heterogeneous network demonstrates the effectiveness and efficiency of our methodology.