2024/07/22 by Sergio Buttazzo, Göran Kauermann, Buttazzo, Sergio +1
Computer Science · Physics and Astronomy · #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2407.15674
openalex publication_date 2024/07/22 · openalex created_date 2024/09/26 · openalex updated_date 2026/07/28
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.