The Mixed-Membership Stochastic Block model (MMSB) is a popular framework for modelling social network relationships which fully exploits each individual node participation (or membership) in a social structure. Despite its powerful representations, this model makes an assumption that the distributions of relational membership indicators between the two nodes are independent. Under many social network settings, however, it is possible that certain known subgroups of people may have higher correlations in terms of their membership categories towards each other, and such prior information should be incorporated into the model. To this end, we introduce a new framework where individual Copula function is to be employed to model jointly the membership pairs of those nodes within the subgroup of interest using Bayesian Non-Parametric methods. Under this framework, various Copula functions may be used to suit the scenario, while maintaining the membership’s marginal distribution, as needed for modeling membership indicators with other nodes outside of the subgroup of interest. We will describe the model in detail and its sampling algorithm for both the finite and infinite (number of categories) case. In this talk, we will also spend some time introducing Bayesian Non-Parametrics.