Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks

TL;DR

This paper introduces a probabilistic tensor factorization model that captures correlations among multiple relation types in multi-relational networks, improving link pattern prediction accuracy.

Contribution

It proposes a novel hierarchical Bayesian probabilistic tensor factorization model with MCMC learning for multi-relational link prediction, addressing overfitting and relation correlation issues.

Findings

Significant improvement over state-of-the-art methods

Effective modeling of multiple relation types

Robust performance on real-world datasets

Abstract

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.