Abstract: In this talk I will introduce a class of latent variable models for relational data. Consider the relational data of pairwise measurements, such as presence or absence of links between a pair of objects. These type of data arises in various environments, for example, Web connects pages by links, collection of author-recipient Emails, scientific literature connects papers by citations, and Social Networks. These models provide exploratory tools for scientific analysis in applications where the observations can be represented as a collection of unipartite graphs. We will also look at a general variational inference algorithm for approximating the intractable posterior distribution.