Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as ``archetypes" characterized by a unique combination of quantitative dimensions that we call ``facets". Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.

翻译：群体——如点的聚类或节点的社区——是处理各种数据挖掘任务的基础。在时间数据中，表征群体演化的主要方法一直是通过识别“事件”。然而，文献中通常描述的事件（例如收缩/增长、分裂/合并）往往定义随意，导致此类理论/预定义类型与真实数据群体观测之间存在脱节。超越现有分类体系，我们将事件视为由我们称之为“侧面”的定量维度的独特组合所表征的“原型”。群体动态由其在该侧面空间中的位置定义，其中原型事件占据极端位置。因此，我们的方法不强制规定严格的事件类型，而是允许涉及群体与多个原型接近度的动态混合描述。我们将该框架应用于来自若干面对面交互数据集的演化群体，结果表明，与现有最先进方法相比，该方法能够对群体动态进行更丰富、更可靠的表征，尤其是在群体关系复杂时。我们的方法还为与动态群体分析相关的常见任务提供了直观解决方案，例如选择适当的聚合尺度、量化分区稳定性以及评估事件质量。