Stochastic Step-wise Feature Selection for Exponential Random Graph
  Models (ERGMs)
Helal El-Zaatari Fei Yu Michael R Kosorok
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.