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.

翻译：社交网络的统计分析为跨学科复杂网络交互提供了宝贵见解。然而，由于计算负担沉重且需考虑观测到的网络依赖关系，网络精准建模仍面临挑战。指数随机图模型作为社交网络建模中一项有前景的技术，通过纳入内生变量来捕捉网络依赖关系。但使用该模型会引发多重挑战，包括ERGMs退化现象——生成不真实且无意义的网络结构。为应对这些挑战并加强协作网络建模，我们提出并测试了一种聚焦于ERGMs内生变量选择的新方法。该方法旨在克服计算负担、优化对观测网络依赖关系的适配能力，从而推动各学科领域对网络现象进行更准确、更有意义的解释。通过实证检验与严格分析，本研究致力于推进统计技术发展，并为网络分析提供实用见解。