Relational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks, while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.

翻译：关系事件网络数据日益丰富，相应的统计模型也随之涌现。这些模型主要聚焦于单一网络的分析，然而在许多应用中，我们观察到多个独立的事件序列，这些序列很可能展现出相似的社会互动动态。此外，关于社会互动行为的假设检验统计方法尚不成熟。因此，本文的贡献有二。首先，我们提出了动态面向行动者模型的多层次扩展，使研究者能够分别对发送者和接收者过程进行建模。多层次框架能够实现跨网络信息的原则性概率借用，从而精确估计社会动态的驱动因素。其次，我们提出了一种灵活的方法，用于检验关于关系事件序列中普遍和异质社会互动驱动因素的假设。我们利用教室中儿童与教师之间的社会互动数据来展示该方法的应用。