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

arXiv: https://arxiv.org/abs/2111.01301 · HTML: https://mlsanity.com/paper/2111.01301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 57 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Concentration Inequalities for Statistical Inference](https://mlsanity.com/paper/2011.02258/) (2020)
- [Elastic-net Regularized High-dimensional Negative Binomial Regression: Consistency and Weak Signals Detection](https://mlsanity.com/paper/1712.03412/) (2017)
- [Sharper Sub-Weibull Concentrations](https://mlsanity.com/paper/2102.02450/) (2021)
- [Maximum entropy distributions on graphs](https://mlsanity.com/paper/1301.3321/) (2013)
- [Asymptotic Theory for Differentially Private Generalized β-models with Parameters Increasing](https://mlsanity.com/paper/2002.12733/) (2020)

## Abstract (arXiv)

For the differential privacy under the sub-Gamma noise, we derive the asymptotic properties of a class of network models with binary values with a general link function. In this paper, 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 asymptotic results.