从连续时间交互数据推断社会结构
Inferring social structure from continuous-time interaction data
摘要：
关系事件数据，其中包括事件涉及对演员随着时间的推移，现在通常可以在最好的时间分辨率。现有的连续时间模型的方法，这些数据是基于点过程和直接模型的相互作用“传染”，其中一个相互作用增加的倾向，未来的相互作用之间的演员，往往是由一些潜在的变量结构。在这篇文章中，我们提出了一种替代方法，使用时间关系点过程模型的连续时间事件数据。我们将一对演员之间的互动描述为虚假的或潜在的社会网络中的潜在的持久连接所导致的。我们认为，与预期行为的一致偏差，而不仅仅是高频率计数，对于识别建立良好的潜在社会关系至关重要。本研究旨在探讨这些潜在的网络结构在两个方面：一个包括大学生和另一个涉及家燕。
英文摘要：
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 that 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.
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