arbiter
JournalsPricingAccount

Using LASSO for variable selection in exponential random graph models

Sergio Buttazzo & Göran Kauermann

Social Networks2026https://doi.org/10.1016/j.socnet.2025.12.007article
AJG 2
Weight
0.50

What the paper says

Exponential Random Graph Models (ERGMs) are a powerful and flexible framework for modeling network data. A fundamental challenge in ERGM estimation is the correct specification of the (sufficient) statistics that define the model structure. This paper addresses the problem of variable selection in ERGMs by making use of LASSO, a penalized estimation technique that shrinks some parameter estimates to zero, effectively selecting relevant variables. While LASSO is well established in standard regression settings, its application to ERGMs remains less explored. Here, we demonstrate how LASSO can be employed in the ERGM framework to perform variable selection and propose a ranking procedure to assess the relevance of candidate model terms.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.socnet.2025.12.007

Or copy a formatted citation

@article{sergio2026,
  title        = {{Using LASSO for variable selection in exponential random graph models}},
  author       = {Sergio Buttazzo & Göran Kauermann},
  journal      = {Social Networks},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.socnet.2025.12.007},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Using LASSO for variable selection in exponential random graph models

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.