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Stochastic step-wise feature selection for Exponential Random Graph Models (ERGMs)

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  • Helal El-Zaatari
  • Fei Yu
  • Michael R Kosorok

Abstract

This study introduces a novel methodology for endogenous variable selection in Exponential Random Graph Models (ERGMs) to enhance the analysis of social networks across various scientific disciplines. Addressing critical challenges such as ERGM degeneracy and computational complexity, our method integrates a systematic step-wise feature selection process. This approach effectively manages the intractable normalizing constants characteristic of ERGMs, ensuring the generation of accurate and non-degenerate network models. An empirical application to nine real-life binary networks demonstrates the method’s effectiveness in accommodating network dependencies and providing meaningful insights into complex network interactions. Particularly notable is the adaptability of this methodology to both directed and undirected networks, overcoming the limitations of traditional ERGMs in capturing realistic network structures. The findings contribute to network analysis, offering a robust framework for modeling and interpreting social networks and laying a foundation for future advancements in statistical network analysis techniques.

Suggested Citation

  • Helal El-Zaatari & Fei Yu & Michael R Kosorok, 2024. "Stochastic step-wise feature selection for Exponential Random Graph Models (ERGMs)," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-12, December.
  • Handle: RePEc:plo:pone00:0314557
    DOI: 10.1371/journal.pone.0314557
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    1. William D. Nordhaus, 2001. "The Progress of Computing," Cowles Foundation Discussion Papers 1324, Cowles Foundation for Research in Economics, Yale University.
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