The asymptotic properties of parameter estimators with a difference private degree sequence have been derived in $\beta$‑model with common binary values, but the general asymptotic properties in network models are lacking. Therefore, we will establish the unified asymptotic result including the consistency and asymptotical normality of the parameter estimator in a class of network models with a difference private degree sequence. Simulations are provided to illustrate asymptotic results.

中文翻译：

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一类具有不同私有度序列的网络模型中的渐近性

具有不同私人度数序列的参数估计器的渐近性质已经在具有公共二进制值的$\beta$-model中得到，但缺乏网络模型中的一般渐近性质。因此，我们将在具有不同隐私度序列的一类网络模型中建立包括参数估计量的一致性和渐近正态性在内的统一渐近结果。提供模拟以说明渐近结果。