The Annals of Applied Probability

Exponential random graphs behave like mixtures of stochastic block models

Ronen Eldan and Renan Gross

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We study the behavior of exponential random graphs in both the sparse and the dense regime. We show that exponential random graphs are approximate mixtures of graphs with independent edges whose probability matrices are critical points of an associated functional, thereby satisfying a certain matrix equation. In the dense regime, every solution to this equation is close to a block matrix, concluding that the exponential random graph behaves roughly like a mixture of stochastic block models. We also show existence and uniqueness of solutions to this equation for several families of exponential random graphs, including the case where the subgraphs are counted with positive weights and the case where all weights are small in absolute value. In particular, this generalizes some of the results in a paper by Chatterjee and Diaconis from the dense regime to the sparse regime and strengthens their bounds from the cut-metric to the one-metric.

Article information

Ann. Appl. Probab., Volume 28, Number 6 (2018), 3698-3735.

Received: September 2017
Revised: March 2018
First available in Project Euclid: 8 October 2018

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62F10: Point estimation 05C80: Random graphs [See also 60B20]
Secondary: 62P25: Applications to social sciences 60F10: Large deviations

Random graph exponential random graph models stochastic block models mixture models Johnson–Lindenstrauss lemma


Eldan, Ronen; Gross, Renan. Exponential random graphs behave like mixtures of stochastic block models. Ann. Appl. Probab. 28 (2018), no. 6, 3698--3735. doi:10.1214/18-AAP1402.

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