Kırbız, SerapHızlı, Çağlar2022-03-022022-03-022022Hızlı, Ç., & Kırbız, S. (January 2022). A Bayesian Allocation Model Based Approach to Mixed Membership Stochastic Blockmodels. Applied Artificial Intelligence, pp 1-23. DOI : https://doi.org/10.1080/08839514.2022.20329231087-65450883-9514https://doi.org/10.1080/08839514.2022.2032923https://hdl.handle.net/20.500.11779/1747Although 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.eninfo:eu-repo/semantics/openAccessCommunityInferenceArticle10.1080/08839514.2022.20329232-s2.0-85124183409Q2Q2231WOS:000750893600001