Data and code underlying the publications: 'Configuration models for random directed hypergraphs' and 'Hypercurveball algorithm for sampling hypergraphs with fixed degrees'

This folder contains the code and data used to compare the performance of two algorithms for generating random hypergraphs with prescribed degree sequences. The comparison is conducted by simulating and analyzing the mixing time of each algorithm.Specifically, the folder includes:Python scripts (.py) for generating random directed or undirected hypergraphs using either the Hypercurveball algorithm or the Hyperedge-shuffle algorithm.A Python script (.py) for analyzing the mixing time of each algorithm. For each algorithm, this script outputs a .csv file that contains the perturbation degree at each step of the simulation.25 hypergraph datasets (.csv), containing both undirected and directed hypergraphs.For each dataset: perturbation degree files (.csv), containing the perturbation degree value at each step of a simulation, for both algorithms. Each algorithm is simulated either 10 or 100 times per dataset.A Python script (.py) for computing various statistics of a hypergraph.<br>The folder accompanies these papers:- Yanna J. Kraakman and Clara Stegehuis (2024). Configuration models for random directed hypergraphs. arXiv:2402.06466.- Yanna J. Kraakman and Clara Stegehuis (2024). Hypercurveball algorithm for sampling hypergraphs with fixed degrees. arXiv:2412.05100

本文件夹包含用于对比两种生成指定度序列的随机超图算法性能的代码与数据，本次对比通过模拟并分析各算法的混合时间（mixing time）完成。具体而言，本文件夹包含以下内容：基于Hypercurveball算法或Hyperedge-shuffle算法生成随机有向或无向超图的Python脚本（.py格式）；用于分析各算法混合时间的Python脚本（.py格式），该脚本针对各算法输出.csv格式文件，内含模拟每一步的扰动度（perturbation degree）数值；25个超图数据集（.csv格式），涵盖无向与有向超图；针对每个数据集，包含两类算法各自的模拟扰动度文件（.csv格式），内含对应模拟每一步的扰动度数值，每个数据集下各算法分别执行10次或100次模拟；用于计算超图各类统计量的Python脚本（.py格式）。本文件夹配套以下两篇学术论文：Yanna J. Kraakman与Clara Stegehuis（2024）。《随机有向超图的配置模型》，arXiv:2402.06466；Yanna J. Kraakman与Clara Stegehuis（2024）。《用于采样固定度超图的Hypercurveball算法》，arXiv:2412.05100