新兴活动时序超图：一种生成真实时变超图的模型
Emerging Activity Temporal Hypergraph: a model for generating realistic time-varying hypergraphs
AI总结：
本文提出新兴活动时序超图模型EATH，通过节点独立活动动态与记忆机制生成保留经验数据统计特征的合成时变超图，并验证其在高阶传染模拟和可调性质生成中的有效性。
摘要：
时变群体交互构成了许多复杂系统的基本构件。时序超图框架通过考虑交互的高阶性和时序性，使得表示这些交互成为可能。然而，相应的数据集往往不完整和/或在规模与持续时间上有限，能够再现其统计特征的替代时变超图构成了有价值的替代方案，尤其是用于理解动力学过程如何在群体交互上展开。在此，我们提出一个新的时序超图模型，即新兴活动时序超图（EATH），该模型可由数据集中测得的参数驱动，并生成具有相似性质的合成数据集。在该模型中，每个节点具有独立的底层活动动态，整体系统活动从节点动态中涌现，时序群体交互由节点活动和记忆机制共同产生。我们首先表明，EATH模型能够为多个面对面交互的经验数据集生成替代超图，在节点和超边层面模拟时序与拓扑性质。我们还展示了将所得合成数据用于高阶传染动力学模拟的可能性，比较了该过程在原始数据集和替代数据集上的结果。最后，我们说明了该模型的灵活性，它能够生成具有可调性质的合成超图：作为一个例子，我们生成了“混合”时序超图，混合了不同经验数据集的性质。我们的工作开辟了多个前景，从生成描述数据收集困难情境的合成真实超图，到更深入地理解时序超图上的动力学过程。
英文摘要：
Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-order and temporal nature of the interactions. However, the corresponding datasets are often incomplete and/or limited in size and duration, and surrogate time-varying hypergraphs able to reproduce their statistical features constitute interesting substitutions, especially to understand how dynamical processes unfold on group interactions. Here, we present a new temporal hypergraph model, the Emerging Activity Temporal Hypergraph (EATH), which can be fed by parameters measured in a dataset and create synthetic datasets with similar properties. In the model, each node has an independent underlying activity dynamic and the overall system activity emerges from the nodes dynamics, with temporal group interactions resulting from both the activity of the nodes and memory mechanisms. We first show that the EATH model can generate surrogate hypergraphs of several empirical datasets of face-to-face interactions, mimicking temporal and topological properties at the node and hyperedge level. We also showcase the possibility to use the resulting synthetic data in simulations of higher-order contagion dynamics, comparing the outcome of such process on original and surrogate datasets. Finally, we illustrate the flexibility of the model, which can generate synthetic hypergraphs with tunable properties: as an example, we generate "hybrid" temporal hypergraphs, which mix properties of different empirical datasets. Our work opens several perspectives, from the generation of synthetic realistic hypergraphs describing contexts where data collection is difficult to a deeper understanding of dynamical processes on temporal hypergraphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
