Stochastic Step-wise Feature Selection for Exponential Random Graph Models (ERGMs)

TL;DR

This paper introduces a stochastic step-wise feature selection method for ERGMs to improve network modeling accuracy and computational efficiency, addressing issues like degeneracy and dependency handling.

Contribution

The paper presents a novel stochastic step-wise feature selection approach specifically designed for ERGMs, enhancing model stability and interpretability.

Findings

Improved model stability and reduced degeneracy in ERGMs.

Enhanced computational efficiency for large networks.

More accurate representation of network dependencies.

Abstract

Statistical analysis of social networks provides valuable insights into complex network interactions across various scientific disciplines. However, accurate modeling of networks remains challenging due to the heavy computational burden and the need to account for observed network dependencies. Exponential Random Graph Models (ERGMs) have emerged as a promising technique used in social network modeling to capture network dependencies by incorporating endogenous variables. Nevertheless, using ERGMs poses multiple challenges, including the occurrence of ERGM degeneracy, which generates unrealistic and meaningless network structures. To address these challenges and enhance the modeling of collaboration networks, we propose and test a novel approach that focuses on endogenous variable selection within ERGMs. Our method aims to overcome the computational burden and improve the accommodation of observed network dependencies, thereby facilitating more accurate and meaningful interpretations of network phenomena in various scientific fields. We conduct empirical testing and rigorous analysis to contribute to the advancement of statistical techniques and offer practical insights for network analysis.