归因流超图：节点归因高阶交互的时间建模
Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions
AI总结：
本文提出归因流超图（ASHs）工具箱，统一高阶建模与特征丰富扩充，用于表示、挖掘和分析复杂网络，并成功应用于社交网络中高阶同质性及时间路径等复杂现象的分析。
摘要：
网络科学的最新进展催生了两个截然不同的研究方向，旨在扩充和增强复杂网络的表示。第一个方向是高阶建模，旨在关注节点集合之间的连接性而非节点对；第二个方向是特征丰富扩充，将那些由结构外部信息驱动的元素（如节点属性或时间流）整合到网络中。本文提出了一个新颖的工具箱，即归因流超图（ASHs），统一了高阶和特征丰富元素，用于表示、挖掘和分析复杂网络。应用于社交网络分析时，ASHs能够沿拓扑、动态和属性元素表征复杂的社会现象。在真实世界面对面和在线社交媒体交互上的实验表明，ASHs能够轻松实现多项分析，包括高阶群体的同质性、节点相对于其参与的超边的同质性，以及超边之间的时间尊重路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
