PROBABILISTIC LATENT TENSOR FACTORIZATION MODEL FOR LINK PATTERN PREDICTION

This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to single-type link prediction, we attempt here to address the prediction of multiple relations, which we refer to as Link Pattern Prediction (LPP) problem. For that we propose a Probabilistic Latent Tensor Factorization (PLTF) model and furnish the Hierarchical Bayesian treatment of the proposed probabilistic model to avoid overfitting problem. To learn the proposed model we develop an efficient Markov Chain Monte Carlo sampling method. Extensive experiments are conducted on several real world datasets and demonstrate significant improvements over several existing state-of-the-art methods.