Universality of the stochastic block model
This work provides a unifying framework for network inference problems, which is incremental but clarifies relationships across methods.
The paper demonstrates that many popular algorithms for mesoscopic pattern extraction in networks are special cases of the stochastic block model or its generalizations, showing its near-universality.
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.