- E. Xing, Min Hyung Lee, Yan Xia. 2015. 0 : Gaussian Graphical Models and Ising Models: Modeling Networks 2 Structural Learning for Completely Observed Graphical Models 2.1 Gaussian Graphical Models. Abstract: There are two ways of exploring a network. The first is a global approach, in which a statistical model is fit on the whole graph. For instance, one can observe that the degree of each node follow a power law distribution, or explore how clusters or cliques form in the network. This approach is not really a graphical model approach, and is not useful in the actionable point of view. The graphical model approach or the microscopic approach tend to be directed to the individuals. This lecture is mainly on two things: learning the graph structure of a model, specifically a Gaussian graphical model, and learning how they evolve through time.
