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

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