Link prediction in heterogeneous data via generalized coupled tensor factorization

• 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

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...