Stochastic Block Modelling (SBM) involves fitting a generative model of a graph to data \citep*{peixoto_bayesian_2017}. Under SBM nonparametric statistical inference is applied to partition the graph in such a way as to maximise the explanatory power of a fitted model given the observed edges. From the candidates the minimum description length model (i.e. the simplest model) is selected to prevent overfitting. SBMs have been found to produce some of the best results on real-life networks and are capable of identifying several types of network structures in addition to communities. SBMs can also detect hierarchical structures in networks and can be extended to overlapping communities. For the purposes of our analysis, we use a degree corrected SBM that employs a Markov chain Monte Carlo (MCMC) algorithm \citep*{peixoto_efficient_2014} as implemented in python igraph library.
Appendix 2: Tables