AI 中文总结
针对指数随机图模型似然难处理、候选统计量相关性强的问题，提出BERGM Elastic Net方法，结合lasso收缩与ridge稳定化，适配过指定网络模型的推断。
AI 中文摘要
指数随机图模型（ERGM）用于描述网络连边间的依赖关系，但当似然难以处理且候选网络统计量存在强相关性时，推断会变得困难。我们提出BERGM Elastic Net，这是一种自适应经验贝叶斯方法，在贝叶斯ERGM中结合了lasso收缩与ridge稳定化。潜变量公式支持近似交换抽样，而经验贝叶斯更新可根据观测网络调整正则化强度。我们将所提先验与弹性网惩罚似然关联，明确阈值化报告与系数分组的解释，该方法针对包含大量相关结构及协变量效应的过指定网络模型开发。
英文摘要
Exponential random graph models (ERGMs) describe dependence among network ties, but inference becomes difficult when the likelihood is intractable and candidate network statistics are strongly correlated. We introduce BERGM Elastic Net, an adaptive empirical-Bayes approach that combines lasso shrinkage with ridge stabilization in a Bayesian ERGM. A latent-variable formulation supports approximate exchange sampling, while empirical-Bayes updates adapt the amount of regularization to the observed network. We connect the proposed prior to elastic-net penalized likelihood and clarify the interpretation of thresholded reporting and coefficient grouping. The method is developed for over-specified network models containing many related structural and covariate effects.
