Welcome to Hypergraphx’s docs!¶
Hypergraphx (HGX) is a Python library for higher-order network analysis.
🚀 Get Started
- ⚙️ Installation
- 🚀 Quickstart
- 📚 Tutorials
- Basics
- Activity Driven
- Scale-free hypergraph generation
- Sampling from the Hy-MMSBM generative model
- Methods for defining the mesoscale structure of higher-order networks
- Conversions
- Extract Statistically Validated Hypergraph
- Higher-order network motif analysis in hypergraphs
- Random walk on hypergraphs
- Shortest Paths
- Analysis of the simplicial contagion
- Analysis of the multiorder Laplacian matrix
- Directed Measures
- Notebook to replicate the analysis proposed in the Section Data of the paper
📖 API Reference
Project links¶
Data: To appear soon
Get started¶
Install HGX from PyPI or source, then verify it works. |
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Create your first hypergraph and compute basic measures. |
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Notebook-driven examples for workflows and models. |
Higher-order data repository¶
To appear soon
Reference¶
Lotito, Quintino Francesco, et al. “Hypergraphx: a library for higher-order network analysis.” Journal of Complex Networks 11.3 (2023): cnad019.
Contributing¶
HGX is a collaborative project and we welcome suggestions and contributions. If you are interested in contributing to HGX or have any questions about our project, please do not hesitate to reach out to us. We look forward to hearing from you!
HGX Team¶
Project coordinators:
Quintino Francesco Lotito
Federico Battiston
Core members:
Martina Contisciani
Caterina De Bacco
Leonardo Di Gaetano
Luca Gallo
Alberto Montresor
Federico Musciotto
Nicolò Ruggeri
License¶
This project is licensed under the BSD 3-Clause License.
Copyright 2026, HGX-Team