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Tame sparse exponential random graphs

Suman Chakraborty et al.

Bernoulli2026https://doi.org/10.3150/25-bej1926article
ABDC A
Weight
0.50

Abstract

In this paper we obtain a precise estimate of the probability that the sparse binomial random graph contains a large number of vertices in a triangle. We compute the logarithm of this probability up to second order, which enables us to propose an exponential random graph model based on the number of vertices in a triangle. Specifically, by tuning a single parameter, we can with high probability induce any given fraction of vertices in a triangle. Moreover, in the proposed exponential random graph model we derive a large deviation principle for the number of edges. As a byproduct, we propose a consistent estimator of the tuning parameter.

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https://doi.org/https://doi.org/10.3150/25-bej1926

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@article{suman2026,
  title        = {{Tame sparse exponential random graphs}},
  author       = {Suman Chakraborty et al.},
  journal      = {Bernoulli},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3150/25-bej1926},
}

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Tame sparse exponential random graphs

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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