Modeling Relational Data via Latent Factor Blockmodel
This work addresses the challenge of integrating global and local structures in relational data for applications like social network analysis and recommender systems, representing an incremental improvement over existing methods.
The paper tackles the problem of modeling relational data by proposing a novel model that combines latent features and latent block structures to improve prediction performance, and it shows that the LFBM model outperforms state-of-the-art approaches in link prediction and cluster analysis tasks on synthetic and real-world datasets.
In this paper we address the problem of modeling relational data, which appear in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with latent characteristics of objects. To combine the benefits of the previous work, we propose a novel model that can simultaneously incorporate the effect of latent features and covariates if any, as well as the effect of latent structure that may exist in the data. To achieve this, we model the relation graph as a function of both latent feature factors and latent cluster memberships of objects to collectively discover globally predictive intrinsic properties of objects and capture latent block structure in the network to improve prediction performance. We also develop an optimization transfer algorithm based on the generalized EM-style strategy to learn the latent factors. We prove the efficacy of our proposed model through the link prediction task and cluster analysis task, and extensive experiments on the synthetic data and several real world datasets suggest that our proposed LFBM model outperforms the other state of the art approaches in the evaluated tasks.