Durable interactions are ubiquitous in social network analysis and are increasingly observed with precise time stamps. Phone and video calls, for example, are events to which a specific duration can be assigned. We term data encoding interactions with the start and end times ``durational event data''. Recent advances in data collection have enabled the observation of such data over extended periods of time and between large populations of actors. Methodologically, we propose the Durational Event Model, an extension of Relational Event Models that decouples the modeling of event incidence from event duration. Computationally, we derive a fast, memory-efficient, and exact block-coordinate ascent algorithm to facilitate large-scale inference. Theoretical complexity analysis and numerical simulations demonstrate computational superiority of this approach over state-of-the-art methods. We apply the model to physical and digital interactions among college students in Copenhagen. Our empirical findings reveal that past interactions drive physical interactions, whereas digital interactions are influenced predominantly by friendship ties and prior dyadic contact.

翻译：在社会网络分析中，持续交互现象普遍存在，且随着时间戳精度的提高，其观测日益精确。例如，电话和视频通话这类事件均可赋予特定的持续时间。我们将记录交互开始与结束时间的数据称为“持续事件数据”。数据采集技术的最新进展使得我们能够长时间观测大规模群体间的此类数据。在方法论上，我们提出了持续事件模型，该模型是对关系事件模型的扩展，将事件发生与事件持续时间的建模解耦。在计算层面，我们推导出一种快速、内存高效且精确的块坐标上升算法，以支持大规模推断。理论复杂度分析与数值模拟均证明该方法在计算性能上优于现有先进技术。我们将该模型应用于哥本哈根大学生之间的物理与数字交互分析。实证结果表明，过往交互驱动物理交互，而数字交互则主要受友谊纽带和先前二元接触的影响。