2019/12/02 by Namjoon Suh, Xiaoming Huo, Suh, Namjoon +5
Computer Science · Decision Sciences · Medicine · #Diverse Approaches in Healthcare and Education Studies #Educational Technology and Assessment #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reliability and Agreement in Measurement
paper · pdf · doi:10.48550/arxiv.1912.00524
openalex publication_date 2019/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.