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arXiv · 2303.18226

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

Andrea Failla·Salvatore Citraro·Giulio Rossetti
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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.

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Andrea Failla, Salvatore Citraro, Giulio Rossetti. 2023-03-31. Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions. https://arxiv.org/abs/2303.18226

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