  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.
