Latent factor blockmodel for modelling relational data

Sheng Gao 1 Ludovic Denoyer 1 Patrick Gallinari 1 Jun Guo
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
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.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01357563
Contributor : Ludovic Denoyer <>
Submitted on : Tuesday, August 30, 2016 - 10:17:06 AM
Last modification on : Thursday, March 21, 2019 - 2:18:58 PM

Identifiers

Citation

Sheng Gao, Ludovic Denoyer, Patrick Gallinari, Jun Guo. Latent factor blockmodel for modelling relational data. 35th European Conference on IR Research, ECIR 2013, Mar 2013, Moscou, Russia. pp.447-458, ⟨10.1007/978-3-642-36973-5_38⟩. ⟨hal-01357563⟩

Share

Metrics

Record views

133