过滤高阶数据集
Filtering higher-order datasets
AI总结：
针对高阶数据集中交互模式随规模分层的问题，提出按交互规模过滤的分析方法，并在多领域经验数据集上验证其能有效提取尺度依赖的连接模式与有价值信息。
摘要：
许多复杂系统通常包含两个以上节点间的交互，即高阶交互，这能以显著方式改变这些系统的结构。研究人员常假设所有交互都能描绘出高阶数据集结构的一致图景。相比之下，经验系统中个体或实体的连接模式通常按交互规模分层。忽视这一事实会聚合仅存在于特定交互尺度上的连接模式。为了分离这些依赖于尺度的模式，我们提出了一种通过按规模过滤交互来分析高阶数据集的方法。我们将该框架应用于来自三个领域的多个经验数据集，以证明数据从业者能从该方法中获取有价值的信息。
英文摘要：
Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset's structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.
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。
