利用属性超图上的许可游走理解高阶网络结构
Understanding High-Order Network Structure using Permissible Walks on Attributed Hypergraphs
AI总结：
本文提出属性超图上的许可游走框架，通过属性化 $s$-线图与关系图相交刻画高阶结构，并在时间属性的 Reddit 数据集上验证。
摘要：
超图作为理解具有高阶连接的复杂网络的工具，近期成为数学数据科学的研究重点。一个特别相关的问题是，在采用基于游走的技术时，如何利用超图属性所承载的信息。在本工作中，我们关注网络中游走的一种新推广，它能够恢复以往方法，并允许描述超图中的许可游走。许可游走图通过将超图的属性化 $s$-线图与一个尊重关系的图相交来构造。超图线图的属性通常承载来自原始超图的类别属性和时间属性的信息。为了在一个具有时间属性的示例上展示该方法，我们将所提出的框架应用于一个 Reddit 数据集；该数据集由作为超边的讨论串和作为节点的作者组成，并记录发帖时间。
英文摘要：
Hypergraphs have been a recent focus of study in mathematical data science as a tool to understand complex networks with high-order connections. One question of particular relevance is how to leverage information carried in hypergraph attributions when doing walk-based techniques. In this work, we focus on a new generalization of a walk in a network that recovers previous approaches and allows for a description of permissible walks in hypergraphs. Permissible walk graphs are constructed by intersecting the attributed $s$-line graph of a hypergraph with a relation respecting graph. The attribution of the hypergraph's line graph commonly carries over information from categorical and temporal attributions of the original hypergraph. To demonstrate this approach on a temporally attributed example, we apply our framework to a Reddit data set composed of hyperedges as threads and authors as nodes where post times are tracked.
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。
