Graphical models have played an important role in inferring dependence structures, discovering multivariate interactions among high‐dimensional data associated with classes of interest such as disease status, and visualizing their association. When data are modeled with Gaussian Markov random fields, the graphical model is called a Gaussian graphical model. It has been used to investigate the conditional dependency structure between random variables by estimating sparse precision matrices. Although the Gaussian model has been widely applied, the normality assumption is rather restrictive. Hence, several methods have been proposed under assumptions weaker than the Gaussian assumptions to handle continuous, discrete, and mixed data. However, modeling data of heterogeneous classes and multilevel networks still poses challenges. Addressing these challenges stresses open problems and points