Temporal properties of higher-order interactions in social networks

TL;DR

This paper explores the prevalence and dynamics of higher-order interactions in temporal social networks, revealing their heterogeneous, bursty nature and differing formation patterns across social settings.

Contribution

It provides the first comprehensive analysis of higher-order interactions in temporal social networks across diverse real-world datasets.

Findings

Higher-order interactions are widespread and exhibit bursty, heterogeneous dynamics.

Group formation and disaggregation are slower in spontaneous settings and faster in work environments.

Longer group durations increase the likelihood of interaction pattern persistence.

Abstract

Human social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people. Such interactions, approximating face-to-face communications, can be effectively represented as time varying social networks with links being unceasingly created and destroyed over time. Traditional analyses of temporal networks have addressed mostly pairwise interactions, where links describe dyadic connections among individuals. However, many network dynamics are hardly ascribable to pairwise settings but often comprise larger groups, which are better described by higher-order interactions. Here we investigate the higher-order organizations of temporal social networks by analyzing three publicly available datasets collected in different social settings. We find that higher-order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics, with bursty trains of rapidly recurring higher-order events separated by long periods of inactivity. We investigate the evolution and formation of groups by looking at the transition rates between different higher-order structures. We find that in more spontaneous social settings, group are characterized by slower formation and disaggregation, while in work settings these phenomena are more abrupt, possibly reflecting pre-organized social dynamics. Finally, we observe temporal reinforcement suggesting that the longer a group stays together the higher the probability that the same interaction pattern persist in the future. Our findings suggest the importance of considering the higher-order structure of social interactions when investigating human temporal dynamics.