Link Prediction via Generalized Coupled Tensor Factorisation
This work addresses link prediction for entities in heterogeneous datasets, representing an incremental improvement through coupled factorisation.
The study tackled the missing link prediction problem by proposing Generalized Coupled Tensor Factorisation to analyze relational datasets as matrices and tensors, demonstrating that joint analysis improves performance and that selecting the right loss function and model is crucial for accurate predictions.
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.