论文标题
一个生成具有社区结构的超图的框架
A framework to generate hypergraphs with community structure
论文作者
论文摘要
近年来，HyperGraphs已成为研究具有多体相互作用的系统的强大工具，这些系统无法微不足道地减少到成对。尽管高度结构化生成合成数据的方法已证明是对算法的标准化评估和现实世界网络数据的统计研究的基础，但这些方法几乎是在超图的背景下可用的。在这里，我们提出了一个灵活，有效的框架，用于生成具有许多节点和大型超级预装的超图，这允许指定一般社区结构并调整不同的本地统计数据。我们说明了如何使用我们的模型以所需的特征（分类或拆卸社区，混合或硬社区分配等）进行综合数据，分析社区检测算法，并在结构上类似于现实世界中的数据。克服了对合成超图生成的先前局限性，我们的工作构成了高阶系统的统计建模方面的重大进步。
In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems.
