Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions

Recent advances in network science have resulted in two distinct research directionsaimed at augmenting and enhancing representations for complex networks. The firstdirection, that of high-order modeling, aims to focus on connectivity between sets ofnodes 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 isexternal to the structure, like node properties or the flow of time. This paper proposes anovel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-orderand 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-worldface-to-face and online social media interactions highlight that ASHs can easily allowfor the analyses, among others, of high-order groups’ homophily, nodes’ homophilywith respect to the hyperedges in which nodes participate, and time-respecting pathsbetween hyperedges.