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Asymptotic in a class of network models with an increasing sub-Gamma degree sequence

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  • Jing Luo
  • Haoyu Wei
  • Xiaoyu Lei
  • Jiaxin Guo

Abstract

For differential privacy under sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function. In this article, we release the degree sequences of the binary networks under a general noisy mechanism, with the discrete Laplace mechanism as a special case. We establish the asymptotic result, including both consistency and asymptotically normality, of the parameter estimator when the number of parameters goes to infinity in a class of network models. Simulations and a real-data example are provided to illustrate the asymptotic results.

Suggested Citation

  • Jing Luo & Haoyu Wei & Xiaoyu Lei & Jiaxin Guo, 2025. "Asymptotic in a class of network models with an increasing sub-Gamma degree sequence," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(9), pages 2507-2532, May.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:9:p:2507-2532
    DOI: 10.1080/03610926.2024.2370915
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