  Recent advances in network science have resulted in two distinct research
directions aimed at augmenting and enhancing representations for complex
networks. The first direction, that of high-order modeling, aims to focus on
connectivity between sets of nodes rather than pairs, whereas the second one,
that of feature-rich augmentation, incorporates into a network all those
elements that are driven by information which is external to the structure,
like node properties or the flow of time. This paper proposes a novel toolbox,
that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and
feature-rich elements for representing, mining, and analyzing complex networks.
Applied to social network analysis, ASHs can characterize complex social
phenomena along topological, dynamic and attributive elements. Experiments on
real-world face-to-face and online social media interactions highlight that
ASHs can easily allow for the analyses, among others, of high-order groups'
homophily, nodes' homophily with respect to the hyperedges in which nodes
participate, and time-respecting paths between hyperedges.
