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 five 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.
Highlights
Human social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people
In this paper our goal is to study the heterogeneous dynamics of group interactions by looking at bursty patterns of higher-order structures in temporal networks
We analyze the temporal properties of multi-party faceto-face interactions[36] recorded in the Sociopatterns project[37], and co-location contacts recorded by Bluetooth technology in the Copenhagen Network Study (CNS)[38] and in the Friends and family (F&F) project[39]
Summary
Human social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people. Consecutive interactions may not appear independently but follow each other rapidly forming bursty patterns[14] potentially due to intrinsic correlations[15] or via circadian fluctuations of human a ctivity[16] Temporal networks describe such processes at the highest time resolution to understand how single interactions may lead to collective phenomena, as long trains of bursty events, or the emergence of the complex social structure. Network and statistical physics approaches were originally devised to describe dyadic r elationships[17,18] and can only provide a limited representation of systems interacting beyond pairwise schemes Such higher-order interactions are u biquitous[19], from human to technological and biological s ystems[20,21]. By analyzing their duration and the time between their subsequent appearances we identify long bursty trains of recurrent group interactions due to temporal correlations
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