Local Community Detection Based on Bayesian Posterior Model

The node centric local community detection plays an important role in the analyis of big data social network. Aiming at the shortcoming of community detection with Newman modularity, an BS modularity based on Bayesian posterior model is proposed in this work to obtain a new local community detection method. It combines Newman modularity with nodes’ recommending probabilities and takes adjacency merge as the framework. It is shown that the proposed algorithm can overcome the shortcomings of the Newman modularity, e.g., lower differentiation in sparse network and worse resolution on community structure and obtain the local community in large scale network. Comparing experiments with Newman modularity on benchmark data validate the BS modularity.