Webpage under development

Open infrastructure for higher-order networks

Hypergraphx ecosystem

Software, datasets, and shared formats for studying systems where interactions happen in groups, not only in pairs.

Library Python toolkit Data curated catalog

Hypergraphx library Dataset catalog

Product 01

Hypergraphx

Python tools for building, analyzing, modeling, and visualizing higher-order networks.

Analysis Models Visualization

Open library site

$ python
import hypergraphx as hgx

H = hgx.Hypergraph(
    [[1, 2, 3], [2, 4], [3, 4, 5]],
    weighted=True,
)

print(H.num_nodes(), H.num_edges())
hgx.draw(H)

Product 02

Hypergraphx-data

A curated catalog of real-world higher-order datasets with metadata and reproducible downloads.

Metadata Downloads Citations

Browse datasets

$ python
from hypergraphx_data import load_dataset

dataset = load_dataset("coauth-pacs")
H = dataset.to_hypergraph()

print(dataset.domain)
print(H.num_nodes(), H.num_edges())

Social Biology Technology Finance

Python Analysis, models, dynamics, visualization

Datasets Curated higher-order networks with metadata

Formats Interoperable JSON and HIF workflows

Ecosystem

One home for the HGX tools

Software GitHub

Hypergraphx

A research-friendly Python library for constructing, storing, analyzing, and visualizing hypergraphs with weighted, directed, temporal, signed, and multiplex interactions.

Analysis Models Visualization

pip install hypergraphx

Open library site

Data GitHub

Hypergraphx-data

A curated catalog of real-world higher-order network datasets spanning social systems, biology, finance, technology, food, and culture, with consistent metadata and ready-to-use downloads.

Social Biology Technology

load_hypergraph_from_server("DATASET_NAME")

Browse datasets

Scope

Built for higher-order network research

Represent

Hypergraphs, temporal hypergraphs, multiplex structures, line graphs, duals, and clique expansions.

Measure

Degrees, centralities, correlations, assortativity, motifs, communities, and mesoscale organization.

Model

Generative models, filtering, inference, diffusion, contagion, random walks, and synchronization.

Team

The HGX team

Contributors

People contributing to the HGX ecosystem

Lead developer

Quintino Francesco Lotito

Post-doctoral Researcher, Central European University

Profile

Project coordinator

Federico Battiston

Full Professor, Central European University

Profile

Martina Contisciani

Library contributor; Postdoctoral Researcher, Center for Critical Computational Studies

Website

Caterina De Bacco

Library contributor; Associate Professor, TU Delft

Website

Leonardo Di Gaetano

Library contributor

Luca Gallo

Library contributor; Postdoctoral Research Fellow, Corvinus University of Budapest

Website

Alberto Montresor

Library and data contributor; Full Professor, University of Trento

Website

Federico Musciotto

Library contributor; University of Palermo

Nicolo Ruggeri

Library contributor; ETH Zurich / Max Planck Institute alumnus

Website

Lorenzo Betti

Hypergraphx-data contributor

Berne Nortier

Hypergraphx-data contributor

GitHub

Alberto Ceria

Library contributor

Davide Colosimo

Library contributor

Helcio Felippe

Library contributor

Alec Kirkley

Library contributor

Alberto Vendramini

Library contributor

References

Core publications

Journal of Complex Networks, 2023

Hypergraphx: a library for higher-order network analysis

Hypergraphx introduces an open-source Python library for building, converting, analyzing, modeling, and visualizing higher-order networks.

Publication

@article{lotito2023hypergraphx,
  title = {Hypergraphx: a library for higher-order network analysis},
  author = {Lotito, Quintino Francesco and Contisciani, Martina and De Bacco, Caterina and Di Gaetano, Leonardo and Gallo, Luca and Musciotto, Federico and Montresor, Alberto and Ruggeri, Nicol{\`o} and Battiston, Federico},
  journal = {Journal of Complex Networks},
  volume = {11},
  number = {3},
  pages = {cnad019},
  year = {2023},
  doi = {10.1093/comnet/cnad019}
}

arXiv, 2026

Hypergraphx-data: a repository for higher-order network data

Hypergraphx-data provides a curated repository of real-world hypergraph datasets with metadata, reproducible downloads, and a public browsing interface.

Publication

@misc{lotito2026hypergraphxdata,
  title = {Hypergraphx-data: a repository for higher-order network data},
  author = {Lotito, Quintino Francesco and Betti, Lorenzo and Nortier, Bern{\'e} and Montresor, Alberto and Battiston, Federico},
  year = {2026},
  eprint = {2605.18166},
  archivePrefix = {arXiv},
  primaryClass = {physics.soc-ph},
  url = {https://arxiv.org/abs/2605.18166}
}

Related projects

Interoperability with the wider ecosystem

{ "network-type": "hypergraph", "nodes": [...], "edges": [...], "metadata": {...} }

Interchange format

HIF

The Hypergraph Interchange Format is a related community standard for moving higher-order network data across software packages. The Network Science paper introduces the JSON-based specification and shows how it supports reproducible interoperability workflows.

@article{coll2025hif,
  title = {HIF: The hypergraph interchange format for higher-order networks},
  author = {Coll, Mart{\'i}n and Joslyn, Cliff A. and Landry, Nicholas W. and Lotito, Quintino Francesco and Myers, Audun and Pickard, Joshua and Praggastis, Brenda and Szufel, Przemys{\l}aw},
  journal = {Network Science},
  volume = {13},
  pages = {e21},
  year = {2025},
  doi = {10.1017/nws.2025.10018}
}

Contribute

Help improve higher-order network tooling

Open an issue, contribute code, propose datasets, or use the tools in your research and cite the corresponding project.

HGX-Team on GitHub Documentation

Acknowledgment

Grants and funding

This project acknowledges support from [Funding agency / grant name], [Institution / research center], and [Collaborating initiative].

Additional acknowledgments, project numbers, institutional statements, and required funding text can be added here.