Higher-order modeling of face-to-face interactions
This work addresses the need for higher-order models of human social interactions, providing a framework that captures group-level phenomena for researchers studying social dynamics.
The authors propose a model for face-to-face interactions that captures group dynamics beyond dyadic relationships, reproducing properties such as group size distribution, correlation, and persistence, which dyadic models cannot replicate.
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.