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Affiliation weighted networks with a differentially private degree sequence

Author

Listed:
  • Jing Luo

    (South Central University for Nationalities)

  • Tour Liu

    (Tianjin Normal University)

  • Qiuping Wang

    (Central China Normal University)

Abstract

Affiliation network is one kind of two-mode social network with two different sets of nodes (namely, a set of actors and a set of social events) and edges representing the affiliation of the actors with the social events. The asymptotic theorem of a differentially private estimator of the parameter in the private $$p_{0}$$ p 0 model has been established. However, the $$p_{0}$$ p 0 model only focuses on binary edges for one-mode network. In many case, the connections in many affiliation networks (two-mode) could be weighted, taking a set of finite discrete values. In this paper, we derive the consistency and asymptotic normality of the moment estimators of parameters in affiliation finite discrete weighted networks with a differentially private degree sequence. Simulation studies and a real data example demonstrate our theoretical results.

Suggested Citation

  • Jing Luo & Tour Liu & Qiuping Wang, 2022. "Affiliation weighted networks with a differentially private degree sequence," Statistical Papers, Springer, vol. 63(2), pages 367-395, April.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:2:d:10.1007_s00362-021-01243-2
    DOI: 10.1007/s00362-021-01243-2
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    References listed on IDEAS

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