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Degree corrected stochastic block model: excursion representation

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arxiv 2409.18894 v1 pith:IUF2IQJ7 submitted 2024-09-27 math.PR

classification math.PR
keywords connecteddcsbmmodelrandomstochasticaldousblockcomponent
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This is the first of two complementary works in which we analyze the connected components of the degree-corrected stochastic block model (DCSBM). Our model is a random graph with an underlying community structure and degree in-homogeneity. It belongs to a class of non-rank one models. The scaling limit of connected component sizes in the near-critical regime, obtained by Konarovskyi and Limic (2021) for a subfamily of DCSBM, is non-trivially different (although related to) the standard eternal multiplicative coalescent of Aldous (1997). The Aldous (1997) excursion representation combined with weak convergence approach to the scaling limits of connected components of random graphs proved to be much more difficult (and therefore rare) for non rank-one models. In this work we show how to build a random field encoding for the connected component structure of DCSBM, in part relying on the theory of Chaumont and Marolleau (2020). We then show how one can, under additional assumptions, reformulate the minimization problem stated in terms of multidimensional first hitting times into an equivalent minimization problem stated for a single real-valued stochastic process. This reformulation relies on a novel composition-like operator on pairs of compatible non-decreasing rcll functions, which might be of independent interest.

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  1. A central limit theorem for the giant in a stochastic block model

    math.PR 2025-01 conditional novelty 6.0 of 10

    Derives an explicit Gaussian central limit theorem for the giant component in a supercritical finite-type stochastic block model, via the excursion representation of the breadth-first walk.

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