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Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models

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  • Ludkin, Matthew

Abstract

The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network.

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  • Ludkin, Matthew, 2020. "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:csdana:v:152:y:2020:i:c:s0167947320301420
    DOI: 10.1016/j.csda.2020.107051
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    Cited by:

    1. Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Weighted stochastic block model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1365-1398, December.

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