Graphical models for large scale data mining constitute an exciting development in statistical data analysis which has gained significant momentum in the past decade. Unlike traditional statistical models which often make `i.i.d.' assumptions, graphical models acknowledge dependencies among variables of interest and investigate inference/prediction while taking into account such dependencies. In recent years, latent variable Bayesian networks, such as latent Dirichlet allocation, stochastic block models, Bayesian co-clustering, and probabilistic matrix factorization techniques have achieved unprecedented success in a variety of application domains including topic modeling and text mining, recommendation systems, multi-relational data analysis, etc. The tutorial will give a broad overview of graphical models, and discuss recent developments in the context of mixed-membership models, matrix analysis models, and their generalizations. The tutorial will present a balanced mix of models, inference/learning methods, and applications.