Link prediction in heterogeneous data via generalized coupled tensor factorization

This study deals with missing link prediction, the problem of predicting the existence of missing connections between entities of interest. We approach the problem as filling in missing entries in a relational dataset represented by several matrices and multiway arrays, that will be simply called te...

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Bibliographic Details
Published in:	Data mining and knowledge discovery Vol. 29; no. 1; pp. 203 - 236
Main Authors:	Ermiş, Beyza, Acar, Evrim, Cemgil, A. Taylan
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01-01-2015 Springer Nature B.V
Subjects:	Arrays Artificial Intelligence Chemistry and Earth Sciences Computer Science Data mining Data Mining and Knowledge Discovery Datasets Experiments Factorization Generalized linear models Information Storage and Retrieval Joining Joints Links Mathematical analysis Mathematical models Missing data Physics Recommender systems Social network analysis Social networks Statistics for Engineering Tensors Link prediction Missing data Data fusion Coupled tensor factorization Heterogeneous data
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