Inferring Block Structure of Graphical Models in Exponential Families
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:939-947, 2015.
Learning the structure of a graphical model is a fundamental problem and it is used extensively to infer the relationship between random variables. In many real world applications, we usually have some prior knowledge about the underlying graph structure, such as degree distribution and block structure. In this paper, we propose a novel generative model for describing the block structure in general exponential families, and optimize it by an Expectation-Maximization(EM) algorithm with variational Bayes. Experimental results show that our method performs well on both synthetic and real data. Further, our method can predict overlapped block structure of a graphical model in general exponential families.