  Directional and pairwise measurements are often used to model
inter-relationships in a social network setting. The Mixed-Membership
Stochastic Blockmodel (MMSB) was a seminal work in this area, and many of its
capabilities were extended since then. In this paper, we propose the
\emph{Dynamic Infinite Mixed-Membership stochastic blockModel (DIM3)}, a
generalised framework that extends the existing work to a potentially infinite
number of communities and mixture memberships for each of the network's nodes.
This model is in a dynamic setting, where additional model parameters are
introduced to reflect the degree of persistence between one's memberships at
consecutive times. Accordingly, two effective posterior sampling strategies and
their results are presented using both synthetic and real data.
