Modeling Relational Data via Latent Factor Blockmodel

TL;DR

This paper introduces a novel latent factor blockmodel that combines latent features and local structure to improve relational data modeling, with applications in social networks, recommender systems, and bioinformatics.

Contribution

It proposes a new model integrating latent features and block structures, along with an optimization algorithm, to enhance relational data analysis.

Findings

Outperforms state-of-the-art methods in link prediction.

Effectively captures both global and local network structures.

Proven on synthetic and real-world datasets.

Abstract

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.