  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.
