The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Silva Fernandes, Sd (Autor) Outra; Tork, HF (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: 2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Páginas: 99-108

Indexação

Scopus - 11 Citações

Outras Informações

ID Authenticus: P-00N-H11

DOI: 10.1109/dsaa.2017.83

Abstract (EN): Link prediction is the task of social network analysis whose goal is to predict the links that will appear in the network in future instants. Among the link predictors exploiting the time evolution of the networks, we can find the tensor decomposition-based methods. A major limitation of these methods is the lack of appropriate approaches for estimating their parameters and initialization. In this paper, we address this problem by proposing a parameter setting method. Our proposed approach resorts to optimization techniques to drive the search for an adequate parameter and initialization choice. © 2017 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica