Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1747
Title: | A bayesian allocation model based approach to mixed membership stochastic blockmodels | Authors: | Hızlı, Çağlar Kırbız, Serap |
Keywords: | Community Inference |
Publisher: | Taylor and Francis Ltd. | Source: | Hızlı, 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/10.1080/08839514.2022.2032923 | 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. | URI: | https://hdl.handle.net/20.500.11779/1747 https://doi.org/10.1080/08839514.2022.2032923 |
ISSN: | 0883-9514 1087-6545 |
Appears in Collections: | Elektrik Elektronik Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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08839514.2022.pdf | Full Text - Article | 3.61 MB | Adobe PDF | View/Open |
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