Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

Abstract

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.

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

“…Fu et al [48] design a state space mixed membership stochastic block model with crossing time. Moreover, to improve the embedding performance of MMSB and fully describe community evolution over time, Yu et al [56] introduce community level to MMSB. They combine the discrete fragmentation coagulation process (DFCP) into their framework to relax the constraints of fixed size compatibility matrix over time in MMSB.…”

TNE: A general time-aware network representation learning framework for temporal applications

“…Other temporal dynamics include periodic effects such as a user's active time in a day/month/year and seasonal effects [59]. Temporal dynamics are of great importance for many real-world temporal applications [25] such as similarity search [19], recommendations [25], link prediction [67], influence modelling [30], community detection [4,21,63], relation reasoning [53], and other network analysis tasks [54].…”