Supplementary code for "Core-periphery Models for Hypergraphs"

Setup

Install required packages with

pip install -r requirements.txt

Download data from Zenodo and set the DATA_ROOT variable in base.py to point at the data.

The options for running the goodness-of-fit experiments can be found with

python goodness_of_fit.py --help

Examples

python goodness_of_fit.py --name threads-math-sx-filtered --learnable_ranks --pipeline cigam -H 0.5,1 --order_max 2 --k_core 2

Zenodo Links

• Datasets
• Source Code

Citation

Please cite the paper, data and source code as

@inproceedings{cigam_paper,
  title		= {Core-periphery Models for Hypergraphs},
  author	= {Papachristou, Marios and Kleinberg, Jon},
  booktitle	= {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining},
  year		= {2022}
}

@dataset{cigam_datasets,
  author       = {Papachristou, Marios and Kleinberg, Jon},
  title        = {Datasets - Core-periphery Models for Hypergraphs},
  month        = feb,
  year         = 2022,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.5943044},
  url          = {https://doi.org/10.5281/zenodo.5943044}
}

@software{cigam_source_code,
  author       = {Papachristou, Marios and Kleinberg, Jon},
  title        = {Code - Core-periphery Models for Hypergraphs},
  month        = feb,
  year         = 2022,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.5965856},
  url          = {https://doi.org/10.5281/zenodo.5965856}
}