Publication :
On the universality of the stochastic block model

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Date
2018-09-24
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Direction de recherche
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Éditeur
American Physical Society
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Résumé
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.
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Revue
Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics, Vol. 98 (3), 1-10 (2018)
DOI
10.1103/PhysRevE.98.032309
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Type de document
article de recherche