Link Prediction via Generalized Coupled Tensor Factorisation

TL;DR

This paper introduces a probabilistic tensor factorisation method for link prediction that leverages coupled analysis of heterogeneous datasets, improving accuracy by jointly modeling multiple data sources.

Contribution

It proposes Generalised Coupled Tensor Factorisation, a novel approach that fits various tensor models with shared latent factors for better link prediction.

Findings

Joint analysis of multiple data sources enhances prediction accuracy.

Choosing appropriate loss functions and tensor models is critical.

Numerical experiments confirm the effectiveness of the proposed method.

Abstract

This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links.