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

Applied Network Science

Research

Andrea Failla¹  Salvatore Citraro²  Giulio Rossetti² 

[1] Department of Computer Science, University of Pisa, Pisa, Italy;Institute of Information Science and Technologies “A. Faedo” (ISTI), National Research Council (CNR), Pisa, Italy;Institute of Information Science and Technologies “A. Faedo” (ISTI), National Research Council (CNR), Pisa, Italy;

关键词: High-order networks;    Feature-rich networks;    Attributed networks;    Stream graphs;   

DOI  :  10.1007/s41109-023-00555-6

 received in 2023-03-31, accepted in 2023-05-28,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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