Hızlı, Çağlar and Kırbız, Serap (2022) A Bayesian Allocation Model Based Approach to Mixed Membership Stochastic Blockmodels. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514
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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.
Item Type: | Article |
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Subjects: | Open STM Article > Computer Science |
Depositing User: | Unnamed user with email support@openstmarticle.com |
Date Deposited: | 17 Jun 2023 07:44 |
Last Modified: | 18 Jun 2024 07:25 |
URI: | http://asian.openbookpublished.com/id/eprint/1073 |