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Bayesian estimation of the latent dimension and communities in stochastic blockmodels

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Title: Bayesian estimation of the latent dimension and communities in stochastic blockmodels
Authors: Sanna Passino, F
Heard, N
Item Type: Journal Article
Abstract: Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.
Issue Date: 1-Sep-2020
Date of Acceptance: 10-May-2020
URI: http://hdl.handle.net/10044/1/80141
DOI: 10.1007/s11222-020-09946-6
ISSN: 0960-3174
Publisher: Springer (part of Springer Nature)
Start Page: 1291
End Page: 1307
Journal / Book Title: Statistics and Computing
Volume: 30
Issue: 5
Copyright Statement: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.
Keywords: cs.SI
Statistics & Probability
0104 Statistics
0802 Computation Theory and Mathematics
Publication Status: Published
Online Publication Date: 2020-05-27
Appears in Collections:Statistics
Faculty of Natural Sciences