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Latent Factor BlockModel for Modelling Relational Data

  • Sheng Gao
  • Ludovic Denoyer
  • Patrick Gallinari
  • Jun Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)

Abstract

In this paper we address the problem of modelling relational data, which has appeared in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models to do link prediction in the relational data but disregarding local structure in the network, or focus exclusively on capturing network structure of objects based on latent blockmodels without coupling with latent characteristics of objects to avoid redundant information. To combine the benefits of the previous work, we model the relational data as a function of both latent feature factors and latent cluster memberships of objects via our proposed Latent Factor BlockModel (LFBM) to collectively discover globally predictive intrinsic properties of objects and capture the latent block structure. We also develop an optimization transfer algorithm to learn the latent factors. Extensive experiments on the synthetic data and several real world datasets suggest that our proposed LFBM model outperforms the state-of-the-art approaches for modelling the relational data.

Keywords

Relational Data Latent Feature Side Information Nonnegative Matrix Factorization Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sheng Gao
    • 1
    • 2
  • Ludovic Denoyer
    • 1
  • Patrick Gallinari
    • 1
  • Jun Guo
    • 2
  1. 1.LIP6 - Université Pierre et Marie CurieFrance
  2. 2.PRIS - Beijing University of Posts and TelecommunicationsChina

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