并非所有纽带都生而平等：面向关系事件数据的二元潜类别模型
Not All Bonds Are Created Equal: Dyadic Latent Class Models for Relational Event Data
AI总结：
针对关系事件建模中行动者层面潜结构限制邻接矩阵表达的问题，本文提出DLC-REM，在二元层面刻画未观测异质性，并通过模拟和国际冲突数据验证其通用性与适用性。
摘要：
动态社会网络可以被概念化为个体之间随时间发生的二元交互序列。在实证社会网络研究中，关系事件模型一直是分析此类交互序列的主力工具。在处理交互机制中可能存在的未观测异质性时，随机块模型等标准方法旨在对行动者层面的变异进行聚类。尽管这些方法有用，但其所隐含的邻接矩阵潜结构具有较强限制性，可能导致有偏的解释和洞见。为解决这一缺陷，我们提出一种更灵活的二元潜类别关系事件模型（DLC-REM），用于在二元层面捕捉未观测异质性。通过数值模拟，我们提供了概念验证，证明该方法比潜行动者层面方法更具一般性。为说明该模型的适用性，我们将其应用于国家间军事化国际冲突的数据集。
英文摘要：
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.
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