Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks
This addresses the problem of predicting complex link patterns in networks with multiple relation types, which is incremental as it extends existing single-type models to multi-relational settings.
The paper tackles link pattern prediction in multi-relational networks by proposing a Probabilistic Latent Tensor Factorization model to capture correlations among relation types, achieving significant improvements over state-of-the-art methods in experiments on real-world datasets.
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 capture the correlations among different relation types and reveal the impact of various relation types on performance quality. For that, we define the overall relations between object pairs as a \textit{link pattern} which consists in interaction pattern and connection structure in the network, and then use tensor formalization to jointly model and predict the link patterns, which we refer to as \textit{Link Pattern Prediction} (LPP) problem. To address the issue, we propose a Probabilistic Latent Tensor Factorization (PLTF) model by introducing another latent factor for multiple relation types and furnish the Hierarchical Bayesian treatment of the proposed probabilistic model to avoid overfitting for solving the LPP 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.