  The Mixed-Membership Stochastic Blockmodel~(MMSB) is proposed as one of the
state-of-the-art Bayesian relational methods suitable for learning the complex
hidden structure underlying the network data. However, the current formulation
of MMSB suffers from the following two issues: (1), the prior information~(e.g.
entities' community structural information) can not be well embedded in the
modelling; (2), community evolution can not be well described in the
literature. Therefore, we propose a non-parametric fragmentation coagulation
based Mixed Membership Stochastic Blockmodel (fcMMSB). Our model performs
entity-based clustering to capture the community information for entities and
linkage-based clustering to derive the group information for links
simultaneously. Besides, the proposed model infers the network structure and
models community evolution, manifested by appearances and disappearances of
communities, using the discrete fragmentation coagulation process (DFCP). By
integrating the community structure with the group compatibility matrix we
derive a generalized version of MMSB. An efficient Gibbs sampling scheme with
Polya Gamma (PG) approach is implemented for posterior inference. We validate
our model on synthetic and real world data.
