Recent advances in data collection and storage have allowed both researchers and industry alike to collect data in real time. Much of this data comes in the form of ‘events’, or timestamped interactions, such as email and social media posts, website clickstreams, or protein–protein interactions. This type of data poses new challenges for modeling, especially if we wish to preserve all temporal features and structure. We highlight several recent approaches in modeling higher-order temporal interaction and bring them together under the umbrella of event graphs. Through examples, we demonstrate how event graphs can be used to understand the higher-order topological-temporal structure of temporal networks and capture properties of the network that are unobservable when considering either a static (or time-aggregated) model. We introduce new algorithms for temporal motif enumeration and provide a novel analysis of the communicability centrality for temporal networks. Furthermore, we show that by modeling a temporal network as an event graph our analysis extends easily to non-dyadic interactions, known as hyper-events.

中文翻译：

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事件图：二阶时间展开时间网络模型的进展和应用

数据收集和存储方面的最新进展使研究人员和行业都能够实时收集数据。这些数据大部分以“事件”或时间戳交互的形式出现，例如电子邮件和社交媒体帖子、网站点击流或蛋白质-蛋白质相互作用。这种类型的数据对建模提出了新的挑战，特别是如果我们希望保留所有时间特征和结构。我们重点介绍了最近建模高阶时间交互的几种方法，并将它们放在事件图的保护伞下。通过示例，我们演示了如何使用事件图来理解时间网络的高阶拓扑时间结构，并捕获在考虑静态（或时间聚合）模型时无法观察到的网络属性。我们介绍了时间主题枚举的新算法，并对时间网络的可通信中心性进行了新的分析。此外，我们表明，通过将时间网络建模为事件图，我们的分析很容易扩展到非二元交互，称为超事件。