A Bayesian Allocation Model Based Approach to Mixed Membership Stochastic Blockmodels. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Hızlı, Çağlar; Kırbız, Serap
• Is Part Of: Applied artificial intelligence. Volume 36:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2022.2032923
• Languages: English
• ISSNs: 0883-9514

Abstract

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.