DNML and NMCL penalized estimators are strongly consistent for estimating the number of communities in sparse stochastic block models where average degree diverges, with DNML being computationally tractable and competitive in unbalanced networks.
(2024) once again to obtain that kY a=1 na n na Γ(na + 1
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Normalized Likelihood Criteria for Model Selection in the Stochastic Block Model
DNML and NMCL penalized estimators are strongly consistent for estimating the number of communities in sparse stochastic block models where average degree diverges, with DNML being computationally tractable and competitive in unbalanced networks.