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February 6, 2026Journal of Statistical Physics0 citationsOpen Access

Large Deviations for Marked Sparse Random Graphs with Applications to Interacting Diffusions

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RBRangel BaldassoRORoberto I. OliveiraAPAlan Pereira

Key Points

  • This research aims to explore the neighborhood distribution of marked sparse Erdős-Rényi random graphs and its implications for interacting diffusions.
  • Analysis of empirical neighborhood distribution in marked sparse Erdős-Rényi graphs
  • Use of BC-entropy for theoretical framework
  • Development of approximation results for graph marks in general spaces
  • Application of results to interacting diffusions on sparse random graphs
  • Proven large deviation principle for empirical neighborhood distribution
  • Developed results applicable to the stochastic Kuramoto model
  • Obtained analogous findings for sparse uniform random graphs with a fixed number of edges

Abstract

Abstract We consider the empirical neighborhood distribution of marked sparse Erdős-Rényi random graphs, obtained by decorating edges and vertices of a sparse Erdős-Rényi random graph with i.i.d. random elements taking values on Polish spaces. We prove that the empirical neighborhood distribution of this model satisfies a large deviation principle in the framework of local weak convergence. We rely on the concept of BC-entropy introduced by Delgosha and Anantharam (2019) which is inspired on the previous work by Bordenave and Caputo (2015). Our main technical contribution is an approximation result that allows one to pass from graph with marks in discrete spaces to marks in general Polish spaces. As an application of the results developed here, we prove a large deviation principle for interacting diffusions driven by gradient evolution and defined on top of sparse Erdős-Rényi random graphs. In particular, our results apply for the stochastic Kuramoto model. We obtain analogous results for the sparse uniform random graph with given number of edges.

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Cite This Study

Baldasso et al. (2026) studied this question.

synapsesocial.com/papers/6985852f8f7c464f230085ebhttps://doi.org/10.1007/s10955-025-03565-z
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