Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
Abstract
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model nor a logistic regression model alone is sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represents the main technological trends (a.k.a., factors), and adds a sparse component that captures the remaining ad-hoc dependence. Parameter estimation is carried out through the construction of a joint-likelihood function of edges and properly chosen penalty terms. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards a low-dimensional latent component and a sparse graphical structure. Simulation results show that the proposed method works well in practical situations. The proposed method has been applied to a real application, which contains a citation network of statisticians (Ji and Jin, 2016). Some interesting findings are reported.
Cite
@article{arxiv.1912.00524,
title = {Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model},
author = {Namjoon Suh and Xiaoming Huo and Eric Heim and Lee Seversky},
journal= {arXiv preprint arXiv:1912.00524},
year = {2019}
}
Comments
Citation network, matrix decomposition, latent variable model, logistic regression model, convex optimization, alternating direction method of multiplier