面对面交互的高阶建模
Higher-order modeling of face-to-face interactions
AI总结：
提出一种基于群体吸引力的高阶模型，再现面对面交互中群体分布、数量相关性和时间持续性等特征，超越传统二元模型。
摘要：
人类最基本的社会互动发生在面对面情境中。近年来，由于个体接近度的高分辨率数据可用性，其特征得到了广泛研究。基于移动主体的数学模型对于理解面对面交互的时空组织至关重要。然而，这些模型仅关注二元关系，无法描述更大群体中的交互特征。在此，我们提出一个模型，其中主体通过形成不同规模的群体进行交互。每个群体具有某种社会吸引力，相邻主体据此决定是否加入。我们的框架再现了面对面交互中群体的不同属性，包括其分布、数量相关性以及时间持续性，而二元模型无法复制这些特征。此外，它捕捉了高阶交互层面的同质性模式，超越了标准的成对方法。我们的工作进一步证明，高阶交互是描述人类面对面接触的关键，为研究微观尺度上的群体动力学如何影响宏观尺度上的社会现象铺平了道路。
英文摘要：
The most fundamental social interactions among humans occur face-to-face. Their features have been extensively studied in recent years, owing to the availability of high-resolution data on individuals' proximity. Mathematical models based on mobile agents have been crucial to understanding the spatio-temporal organization of face-to-face interactions. However, these models focus on dyadic relationships only, failing to characterize interactions in larger groups of individuals. Here, we propose a model in which agents interact with each other by forming groups of different sizes. Each group has a degree of social attractiveness, based on which neighboring agents decide whether to join. Our framework reproduces different properties of groups in face-to-face interactions, including their distribution, the correlation in their number, and their persistence in time, which dyadic models cannot replicate. Furthermore, it captures homophilic patterns at the level of higher-order interactions, going beyond standard pairwise approaches. Our work provides further evidence that higher-order interactions are key to describe human face-to-face contacts, paving the way for further investigation of how group dynamics at a microscopic scale affects social phenomena at a macroscopic scale.
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。
