Consistent model selection for the Degree Corrected Stochastic Blockmodel
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The Degree Corrected Stochastic Block Model (DCSBM) was introduced by <cit.> as a generalization of the stochastic block model in which vertices of the same community are allowed to have distinct degree distributions. On the modelling side, this variability makes the DCSBM more suitable for real life complex networks. On the statistical side, it is more challenging due to the large number of parameters when dealing with community detection. In this paper we prove that the penalized marginal likelihood estimator is strongly consistent for the estimation of the number of communities. We consider dense or semi-sparse random networks, and our estimator is unbounded, in the sense that the number of communities k considered can be as big as n, the number of nodes in the network.
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