Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks

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 : We address the problem of link prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While traditional link prediction models are limited to single-type link prediction we attempt here to jointly model and predict the multiple relation types, which we refer to as the link pattern prediction (LPP) problem. For that, we propose a probabilistic latent tensor factorization (PLTF) model and furnish the Bayesian treatment of the probabilistic model to avoid overfitting problem. To learn the proposed model we develop an efficient Markov chain Monte Carlo (MCMC) sampling method. Extensive experiments on several real world multi-relational datasets demonstrate the significant improvements of our model over several state-of-the-art methods.
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The Journal of China Universities of Posts and Telecommunications, 2012, 19 (2), pp.172-181. 〈10.1016/S1005-8885(11)60425-1〉
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Soumis le : jeudi 20 août 2015 - 11:37:10
Dernière modification le : jeudi 22 novembre 2018 - 14:41:10

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Sheng Gao, Ludovic Denoyer, Patrick Gallinari, Jun Guo. Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks. The Journal of China Universities of Posts and Telecommunications, 2012, 19 (2), pp.172-181. 〈10.1016/S1005-8885(11)60425-1〉. 〈hal-01185466〉

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