  Mesoscopic pattern extraction (MPE) is the problem of finding a partition of
the nodes of a complex network that maximizes some objective function. Many
well-known network inference problems fall in this category, including, for
instance, community detection, core-periphery identification, and imperfect
graph coloring. In this paper, we show that the most popular algorithms
designed to solve MPE problems can in fact be understood as special cases of
the maximum likelihood formulation of the stochastic block model (SBM), or one
of its direct generalizations. These equivalence relations show that the SBM is
nearly universal with respect to MPE problems.
