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Asymptotics in the β-model for networks with a differentially private degree sequence

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  • Lu Pan
  • Ting Yan

Abstract

The β-model is a natural model for characterizing the degree heterogeneity that widely exists in the network data. The estimators of the model parameters in the differentially private β-model with the denoised process have been shown to be consistent and asymptotically normal. In this paper, we show that the moment estimators of the parameters based on the differentially private degree sequence without the denoised process is consistent and asymptotically normal.

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  • Lu Pan & Ting Yan, 2020. "Asymptotics in the β-model for networks with a differentially private degree sequence," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(18), pages 4378-4393, September.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:18:p:4378-4393
    DOI: 10.1080/03610926.2019.1599023
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    Cited by:

    1. Jing Luo & Haoyu Wei & Xiaoyu Lei & Jiaxin Guo, 2021. "Asymptotic in a class of network models with an increasing sub-Gamma degree sequence," Papers 2111.01301, arXiv.org, revised Nov 2023.

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