El-Zaatari Helal, Yu Fei, Kosorok Michael R
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States of America.
Health Sciences Library, University of North Carolina, Chapel Hill, NC, United States of America.
PLoS One. 2024 Dec 17;19(12):e0314557. doi: 10.1371/journal.pone.0314557. eCollection 2024.
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.
本研究引入了一种用于指数随机图模型（ERGMs）中内生变量选择的新方法，以加强跨多个科学学科的社会网络分析。针对诸如ERGM退化和计算复杂性等关键挑战，我们的方法集成了一个系统的逐步特征选择过程。这种方法有效地管理了ERGM特有的难以处理的归一化常数，确保生成准确且非退化的网络模型。对九个实际二元网络的实证应用证明了该方法在适应网络依赖性以及为复杂网络交互提供有意义见解方面的有效性。特别值得注意的是，这种方法对有向和无向网络都具有适应性，克服了传统ERGM在捕捉现实网络结构方面的局限性。这些发现有助于网络分析，为建模和解释社会网络提供了一个强大的框架，并为统计网络分析技术的未来发展奠定了基础。
