Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous-time methods for modeling such data are based on point processes and directly model interaction "contagion," whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal-relational point process models for continuous-time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well-established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.

中文翻译：

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从连续时间交互数据推断社会结构

关系事件数据，包括随着时间的推移涉及成对参与者的事件，现在通常可以在最好的时间分辨率下获得。现有的对此类数据进行建模的连续时间方法基于点过程并直接模拟交互“传染”，其中一种交互增加了参与者之间未来交互的倾向，通常由一些潜在的变量结构决定。在本文中，我们提出了一种将时间关系点过程模型用于连续时间事件数据的替代方法。我们将一对参与者之间的互动描述为虚假的或由潜在社交网络中潜在的、持久的连接产生的。我们认为与预期行为的一致偏差，而不仅仅是高频计数，对确定已建立的潜在社会关系至关重要。本研究旨在在两种情况下探索这些潜在的网络结构：一种包括大学生，另一种涉及家燕。