Link pattern prediction with tensor decomposition in multi-relational networks

Sheng Gao 1 Ludovic Denoyer 1 Patrick Gallinari 1
1 MALIRE - Machine Learning and Information Retrieval
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 tensor decomposition model to solve the LPP problem, which allows to capture the correlations among different relation types and reveal the impact of various relations on prediction performance. The proposed tensor decomposition model is efficiently learned with a conjugate gradient based optimization method. Extensive experiments on real-world datasets demonstrate that this model outperforms the traditional mono-relational model and can achieve better prediction quality.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00687012
Contributor : Sheng Gao <>
Submitted on : Thursday, April 12, 2012 - 1:48:29 AM
Last modification on : Thursday, March 21, 2019 - 1:12:59 PM

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Sheng Gao, Ludovic Denoyer, Patrick Gallinari. Link pattern prediction with tensor decomposition in multi-relational networks. CIDM 2011 - IEEE Symposium on Computational Intelligence and Data Mining, Apr 2011, Paris, France. pp.333-340, ⟨10.1109/CIDM.2011.5949306⟩. ⟨hal-00687012⟩

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