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In the model-based clustering of networks, blockmodelling may be used toidentify roles in the network. We identify a special case of the StochasticBlock Model (SBM) where we constrain the cluster-cluster interactions such thatthe density inside the clusters of nodes is expected to be greater than thedensity between clusters. This corresponds to the intuition behindcommunity-finding methods, where nodes tend to clustered together if they linkto each other. We call this model Stochastic Community Finding (SCF) andpresent an efficient MCMC algorithm which can cluster the nodes, given thenetwork. The algorithm is evaluated on synthetic data and is applied to asocial network of interactions at a karate club and at a monastery,demonstrating how the SCF finds the 'ground truth' clustering where sometimesthe SBM does not. The SCF is only one possible form of constraint orspecialization that may be applied to the SBM. In a more supervised context, itmay be appropriate to use other specializations to guide the SBM.

Model-based clustering in networks with Stochastic Community Finding