Attributed Stream-Hypernetwork Analysis: a SocioPatterns Case Study.

Network science comes as a solid framework to describe a multitude of human behaviours. Face-toface human interactions, for instance, are often represented by dynamic networks involving time-varying links. Such temporal models are shown to be effective as proxies for real communications between individuals. However, networks are intrinsically bounded to pairwise/dyadic connections, whereas complex human dynamics can naturally involve higher-order organization, namely relationships between groups of entities. In the last few years, hypergraph and simplicial complex models have beenaddressed as promising tools to better understand the dynamics of social groups. In the analysisof face-to-face interactions, the higher-order organization of temporal networks has been addressedby investigating collections of datasets initially designed for graph-based analysis. Yet even thesehigher-order representations continue to ignore the rich attributes or metadata often carried by thenodes. Such attributes can offer new interesting perspectives about the dynamics of the higher-orderstructure emerging from a stream of social interactions. In this work, we aim to address this gapby introducing attributed stream-hypernetwork models, i.e., higher-order temporal networks withattributive information on nodes. Considering the Primary and High School temporal networks from thewell-known SocioPatterns project, we infer the higher-order temporal structure of interactions betweenchildren and high school students, and we characterize their non-trivial relationships with respect totheir gender attribute.