  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.
