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

Research output: Contribution to journalJournal articlepeer-review

Published
Article number107051
<mark>Journal publication date</mark>1/12/2020
<mark>Journal</mark>Computational Statistics and Data Analysis
Volume152
Number of pages14
Publication StatusPublished
Early online date24/07/20
<mark>Original language</mark>English

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.