标题： 并非所有关系都相同：用于关系事件数据的二元潜在类别模型 显示英文标题

标题： Not All Bonds Are Created Equal: Dyadic Latent Class Models for Relational Event Data

摘要： 动态社会网络可以被概念化为随时间变化的个体之间的二元交互序列。 关系事件模型是实证社会网络研究中分析这种交互序列的常用方法。 在处理交互机制中的潜在异质性时，标准方法，如随机块模型，旨在在行动者层面对变化进行聚类。 虽然有用，但邻接矩阵的隐含潜在结构具有限制性，这可能导致有偏的解释和见解。 为解决这一不足，我们引入了一种更灵活的二元潜在类别关系事件模型（DLC-REM），该模型在二元层面捕捉潜在异质性。 通过数值模拟，我们提供了一个概念验证，证明这种方法比潜在行动者层面的方法更为普遍。 为了说明该模型的适用性，我们将它应用于国家之间的军事化国际冲突数据集。 显示英文摘要

摘要： Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, standard approaches, such as the stochastic block model, aim to cluster the variation at the actor level. Though useful, the implied latent structure of the adjacency matrix is restrictive which may lead to biased interpretations and insights. To address this shortcoming, we introduce a more flexible dyadic latent class relational event model (DLC-REM) that captures the unobserved heterogeneity at the dyadic level. Through numerical simulations, we provide a proof of concept demonstrating that this approach is more general than latent actor-level approaches. To illustrate the applicability of the model, we apply it to a dataset of militarized interstate conflicts between countries.