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

Abstract

Abstract 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.

Keywords

• High-order networks
• Feature-rich networks
• Attributed networks
• Stream graphs