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A network model that combines latent factors and sparse graphs.
- Source :
- Statistical Analysis & Data Mining; Apr2021, Vol. 14 Issue 2, p97-115, 19p
- Publication Year :
- 2021
-
Abstract
- We propose a combined model, which integrates the latent factor model and a sparse graphical model, for network data. It is noticed that neither a latent factor model nor a sparse graphical model alone may be sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represent the main trends (a.k.a., factors), and a sparse graphical component that captures the remaining ad‐hoc dependence. Model selection and parameter estimation are carried out simultaneously via a penalized likelihood approach. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards low‐dimensional latent components and a sparse graphical structure. The effectiveness of our model is demonstrated via simulation studies, and the model is also applied to four real datasets: Zachary's Karate club data, Kreb's U.S. political book dataset (http://www.orgnet.com), U.S. political blog dataset , and citation network of statisticians; showing meaningful performances in practical situations. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19321864
- Volume :
- 14
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Statistical Analysis & Data Mining
- Publication Type :
- Academic Journal
- Accession number :
- 149246456
- Full Text :
- https://doi.org/10.1002/sam.11492