Using LASSO for Variable Selection in Exponential Random Graph models

TL;DR

This paper explores the application of LASSO-based penalized estimation to select relevant variables in Exponential Random Graph Models, enhancing network data analysis by automating model specification and selection.

Contribution

It introduces LASSO estimation to ERGMs, enabling automatic variable selection and model specification in network analysis.

Findings

LASSO effectively shrinks some parameters to zero, aiding in model simplification.

The method provides a flexible framework for selecting network statistics.

Demonstrates practical application in network data analysis.

Abstract

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.