建模二分动态网络：加性与乘性效应模型

中文摘要 AI 辅助

研究者经常研究两种不同类型行为者之间的互动，这些互动以二分网络表示。这些网络表现出与单模网络不同的依赖模式，因此需要针对其结构定制的模型。本文针对纵向二分数据开发了一个加性和乘性效应（AME）框架。首先，我区分了依赖结构并指定了相应的建模假设。其次，我引入了二分动态AME模型，并开发了一种基于块坐标下降的估计程序。第三，我结合了平方迭代方法以提高计算效率。通过模拟和对全球生产网络的应用，我展示了该模型改进了系数估计，更准确地恢复了数据生成过程，并更好地捕捉了乘性潜在结构。该模型揭示了各国全球生产参与中的演变模式，这些模式未被观测协变量或静态规格所捕捉。我提供了一个R包RAMEN以促进实施。

英文摘要

Researchers frequently study interactions between two distinct types of actors, represented as bipartite networks. These networks exhibit dependence patterns that differ from those in one-mode networks and therefore require models tailored to their structure. This paper develops an additive and multiplicative effects (AME) framework for longitudinal bipartite data. First, I distinguish the dependence structure and specify the corresponding modeling assumptions. Second, I introduce the bipartite dynamic AME model and develop an estimation procedure based on block coordinate descent. Third, I incorporate a squared iterative method to improve computational efficiency. Using simulations and an application to global production networks, I show that the model improves coefficient estimation, more accurately recovers the data-generating process, and better captures the multiplicative latent structure. The model reveals evolving patterns in countries' global production engagement that are not captured by observed covariates or static specifications. I provide an R package, RAMEN, to facilitate implementation.

发表机构

• The Ohio State University（俄亥俄州立大学）

机构由 AI 辅助整理，请以论文原文为准。