Bayesian Community Detection for Weighted Sparse Networks Using Mixture of SBM Model (306428)*YuTzu Kuo, University of Notre Dame
Lizhen Lin, University of Notre Dame
Luyi Shen, University of Notre Dame
Keywords: Sparse network, Bayesian community detection, Weighted network, CRP, MCMC algorithms, SBM model
We propose a novel mixture of stochastic block model for community detection in weighted networks. Our model allows modeling the sparsity of network and performing community detection simultaneously by cleverly combining the spike and slab prior with a stochastic block model. A Chinese restaurant process prior is used for modeling the random partition of the model which does require the number of community to be known as a priori. Another appealing feature of our model is that it allows the sparsity level or the network to vary across communities. That is, the sparsity informational network is incorporated for community detection. Efficient MCMC algorithms are derived for sampling the posterior distribution for inference and our model and algorithms were demonstrated using both simulated and real data sets.