In this paper, we focus on temporal property graphs, that is, property graphs whose labeled nodes and edges as well as the values of the properties associated with them may change with time. A key challenge in studying temporal graphs lies in detecting interesting events in their evolution, defined as time intervals of significant stability, growth, or shrinkage. To address this challenge, we build aggregated graphs, where nodes are grouped based on the values of their properties, and seek events at the aggregated level. To locate such events, we propose a novel approach based on unified evolution skylines. A unified evolution skyline assesses the significance of an event in conjunction with the duration of the interval in which the event occurs. Significance is measured by a set of counts, where each count refers to the number of graph elements that remain stable, are created, or deleted, for a specific property value. Lastly, we share experimental findings that highlight the efficiency and effectiveness of our approach.

在本文中，我们关注时间属性图，即标记的节点和边以及与其相关的属性值可能随时间变化的属性图。研究时间图的一个关键挑战在于检测其演化中的有趣事件，定义为显着稳定性、增长或收缩的时间间隔。为了应对这一挑战，我们构建聚合图，其中节点根据其属性值进行分组，并在聚合级别查找事件。为了定位此类事件，我们提出了一种基于统一进化天际线的新颖方法。统一的演化天际线结合事件发生间隔的持续时间来评估事件的重要性。显着性通过一组计数来衡量，其中每个计数指的是针对特定属性值保持稳定、创建或删除的图形元素的数量。最后，我们分享实验结果，强调我们方法的效率和有效性。