TY - DATA
T1 - Data and code underlying the publications: 'Configuration models for random directed hypergraphs' and 'Hypercurveball algorithm for sampling hypergraphs with fixed degrees'
PY - 2025/04/17
AU - ir. Y.J. Kraakman
AU - Clara Stegehuis
UR - 
DO - 10.4121/9beea11f-2e93-473d-9d22-8d8a6bec9d5a.v1
KW - Hypercurveball
KW - Hyperarc-shuffle
KW - Hyperedge-shuffle
KW - Random hypergraph
KW - Configuration model
N2 - <p>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.</p><p>Specifically, the folder includes:</p><ol><li>Python scripts (.py) for generating random directed or undirected hypergraphs using either the Hypercurveball algorithm or the Hyperedge-shuffle algorithm.</li><li>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.</li><li>25 hypergraph datasets (.csv), containing both undirected and directed hypergraphs.</li><li>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.</li><li>A Python script (.py) for computing various statistics of a hypergraph.</li></ol><p><br></p><p>The folder accompanies these papers:</p><p>- Yanna J. Kraakman and Clara Stegehuis (2024). Configuration models for random directed hypergraphs. arXiv:2402.06466.</p><p>- Yanna J. Kraakman and Clara Stegehuis (2024). Hypercurveball algorithm for sampling hypergraphs with fixed degrees. arXiv:2412.05100 </p>
ER -