  Community detection tasks have received a lot of attention across statistics,
machine learning, and information theory with a large body of work
concentrating on theoretical guarantees for the stochastic block model. One
line of recent work has focused on modeling the spectral embedding of a network
using Gaussian mixture models (GMMs) in scaling regimes where the ability to
detect community memberships improves with the size of the network. However,
these regimes are not very realistic. This paper provides tractable methodology
motivated by new theoretical results for networks with non-vanishing noise. We
present a procedure for community detection using GMMs that incorporates
certain truncation and shrinkage effects that arise in the non-vanishing noise
regime. We provide empirical validation of this new representation using both
simulated and real-world data.
