Data and code underlying the publications: 'Configuration models for random directed hypergraphs' and 'Hypercurveball algorithm for sampling hypergraphs with fixed degrees'
DOI:10.4121/9beea11f-2e93-473d-9d22-8d8a6bec9d5a.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
DOI: 10.4121/9beea11f-2e93-473d-9d22-8d8a6bec9d5a
DOI: 10.4121/9beea11f-2e93-473d-9d22-8d8a6bec9d5a
Datacite citation style
Kraakman, Yanna; Stegehuis, Clara (2025): Data and code underlying the publications: 'Configuration models for random directed hypergraphs' and 'Hypercurveball algorithm for sampling hypergraphs with fixed degrees'. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/9beea11f-2e93-473d-9d22-8d8a6bec9d5a.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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.
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
History
- 2025-04-17 first online, published, posted
Publisher
4TU.ResearchDataFormat
Algorithms: .py, datasets: zipped csv fileFunding
- NWO (grant code P15-36) Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Organizations
University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Mathematical Operation ResearchDATA
Files (10)
- 1,436 bytesMD5:
7aee412ce676b51840e70cfd6431df0bREADME.txt - 3,982 bytesMD5:
7662c8840de4d2c44b93093d8c9ae51achange_notation.py - 17,191 bytesMD5:
a35e529b24c6d9d03cddfee7162f7cb0data_statistics.py - 292,834,629 bytesMD5:
0b322825f7fab66269a66bf4bca5c40cdatasets.zip - 1,777 bytesMD5:
86e084222ed497a981d9d1eba9d387e6example_code.py - 2,004 bytesMD5:
635ff1573c7fb9003bf3bb9b244fc679example_data_edges.py - 3,682 bytesMD5:
d29cb74d2c8002181f8302a7ad466d16hypercurveball.py - 5,891 bytesMD5:
7a01c6692c8de5541856e3e7eae8140ahypershuffle.py - 10,047 bytesMD5:
0fcdd3f84e2062691210311a23931472mixing_time.py - 22,250 bytesMD5:
b710db94de4e6535d008c4471c685ddepossible_examples.py -
download all files (zip)
292,902,889 bytes unzipped





