A Bayesian Allocation Model Based Approach to Mixed Membership Stochastic Blockmodels

ABSTRACT

Although detecting communities in networks has attracted considerable recent attention, estimating the number of communities is still an open problem. In this paper, we propose a model, which replicates the generative process of the mixed-membership stochastic block model (MMSB) within the generic allocation framework of Bayesian allocation model (BAM) and BAM-MMSB. In contrast to traditional blockmodels, BAM-MMSB considers the observations as Poisson counts generated by a base Poisson process and marks according to the generative process of MMSB. Moreover, the optimal number of communities for BAM-MMSB is estimated by computing the variational approximations of the marginal likelihood for each model order. Experiments on synthetic and real data sets show that the proposed approach promises a generalized model selection solution that can choose not only the model size but also the most appropriate decomposition.

Acknowledgments

We thank Dr. A. Taylan Cemgil (Boğaziçi University) for useful discussions. This work is supported by TÜBİTAK grant number 215E076.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Türkiye Bilimsel ve Teknolojik Araştirma Kurumu [215E076].