Affiliation weighted networks with a differentially private degree sequence

Jing Luo (), Tour Liu () and Qiuping Wang ()
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Jing Luo: South Central University for Nationalities
Tour Liu: Tianjin Normal University
Qiuping Wang: Central China Normal University

Statistical Papers, 2022, vol. 63, issue 2, No 2, 367-395

Abstract: 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.

Keywords: Affiliation networks; Asymptotic normality; Consistency; Finite discrete weight; Differential privacy; 62E20; 62F12 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-021-01243-2

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