Open Access
May 2017 Asymptotics for sparse exponential random graph models
Mei Yin, Lingjiong Zhu
Braz. J. Probab. Stat. 31(2): 394-412 (May 2017). DOI: 10.1214/16-BJPS319

Abstract

We study the asymptotics for sparse exponential random graph models where the parameters may depend on the number of vertices of the graph. We obtain exact estimates for the mean and variance of the limiting probability distribution and the limiting log partition function of the edge-(single)-star model. They are in sharp contrast to the corresponding asymptotics in dense exponential random graph models. Similar analysis is done for directed sparse exponential random graph models parametrized by edges and multiple outward stars.

Citation

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Mei Yin. Lingjiong Zhu. "Asymptotics for sparse exponential random graph models." Braz. J. Probab. Stat. 31 (2) 394 - 412, May 2017. https://doi.org/10.1214/16-BJPS319

Information

Received: 1 September 2015; Accepted: 1 April 2016; Published: May 2017
First available in Project Euclid: 14 April 2017

zbMATH: 1365.05264
MathSciNet: MR3635912
Digital Object Identifier: 10.1214/16-BJPS319

Keywords: asymptotics , exponential random graphs , sparse random graphs

Rights: Copyright © 2017 Brazilian Statistical Association

Vol.31 • No. 2 • May 2017
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