The paper demonstrates the use of LASSO-based estimation in network models. Taking the Exponential Random Graph Model (ERGM) as a flexible and widely used model for network data analysis, the paper focuses on the question of how to specify the (sufficient) statistics, that define the model structure. This includes both, endogenous network statistics (e.g. twostars, triangles, etc.) as well as statistics involving exogenous covariates; on the node as well as on the edge level. LASSO estimation is a penalized estimation that shrinks some of the parameter estimates to be equal to zero. As such it allows for model selection by modifying the amount of penalty. The concept is well established in standard regression and we demonstrate its usage in network data analysis, with the advantage of automatically providing a model selection framework.

翻译：本文展示了基于LASSO的估计方法在网络模型中的应用。以指数随机图模型（ERGM）这一灵活且广泛使用的网络数据分析模型为基础，本文重点探讨如何确定定义模型结构的（充分）统计量。这既包括内生网络统计量（如双星结构、三角结构等），也涉及包含外生协变量的统计量；涵盖节点层面与边层面。LASSO估计是一种惩罚性估计方法，可将部分参数估计值压缩至零。通过调整惩罚量，该方法能够实现模型选择。这一概念在标准回归分析中已得到广泛应用，我们展示了其在网络数据分析中的运用，其优势在于能自动提供模型选择框架。