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

Ludkin, M. (2020) Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models. Computational Statistics and Data Analysis, 152. ISSN 0167-9473

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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.

Item Type:
Journal Article
Journal or Publication Title:
Computational Statistics and Data Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
147005
Deposited By:
Deposited On:
02 Sep 2020 10:45
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Sep 2020 05:42