Overview

../_images/harrypotter_basic_hyp.png

HyperNetX

The HyperNetX (HNX) library provides classes and methods for the analysis and visualization of complex network data modeled as hypergraphs. The library generalizes traditional graph metrics. Documentation for HNX is available at: https://hypernetx.readthedocs.io/

HNX was originally developed by the Pacific Northwest National Laboratory for the Hypernets project as part of its High Performance Data Analytics (HPDA) program. It is currently maintained by scientists at PNNL, but contributions and bug fixes from the community are welcome and encouraged. Please see our [Contributor’s Guide](https://hypernetx.readthedocs.io/en/latest/contributions.html) for more information.

PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830.

  • Principal Developer and Designer: Brenda Praggastis

  • Development Team: Brenda Praggastis, Audun Myers, Greg Roek, Ryan Danehy

  • Visualization: Dustin Arendt, Ji Young Yun

  • Principal Investigator: Cliff Joslyn

  • Program Manager: Brian Kritzstein

  • Principal Contributors (Design, Theory, Code): Sinan Aksoy, Dustin Arendt, Mark Bonicillo, Ryan Danehy, Helen Jenne, Cliff Joslyn, Nicholas Landry, Audun Myers, Christopher Potvin, Brenda Praggastis, Emilie Purvine, Greg Roek, Mirah Shi, Francois Theberge, Ji Young Yun

The code in this repository is intended to support researchers modeling data as hypergraphs. We have a growing community of users and contributors. HNX is a primary contributor to the Hypergraph Interchange Format (HIF), a json schema for sharing data modeled as hypergraphs. The specification and sample notebooks may be found here: https://github.com/pszufe/HIF-standard/tree/main Other hypergraph libraries using this standard are listed below:

For questions and comments about HNX contact the developers directly at: hypernetx@pnnl.gov.

HyperNetX 2.3

HyperNetX 2.3. is the latest, stable release. The core library has been refactored to take better advantage of Pandas Dataframes, improve readability and maintainability, address bugs, and make it easier to change. New features have been added, most notably the ability to add and remove edges, nodes, and incidences. Updating is recommended.

Version 2.3 is not backwards compatible. Objects constructed using earlier versions can be imported using their incidence dictionaries and/or property datafames.

What’s New

  1. We’ve added new functionality to Hypergraphs; you can add and remove nodes, edges, and incidences on Hypergraph.

  2. Arithmetic operations have also been added to Hypergraph: sum, difference, union, intersection.

  3. We’ve also added a new tutorial on basic hypergraph arithmetic operations.

  4. Under the hood, the EntitySet has been replaced by HypergraphView, new factory methods have been created to support the Hypergraph constructor, and internal classes such as IncidenceStore and PropertyStore help maintain the structure and attributes of a Hypergraph.

What’s Changed

  1. Documentation has received a major update; the Glossary and docstrings of Hypergraph have been updated.

  2. HNX now requires Python >=3.10,<4.0.0

  3. We’ve upgraded all the underlying core libraries to the latest versions.

COLAB Tutorials

The following tutorials may be run in your browser using Google Colab. Additional tutorials are available on GitHub.

Notice

This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

      PACIFIC NORTHWEST NATIONAL LABORATORY
      operated by
      BATTELLE
      for the
      UNITED STATES DEPARTMENT OF ENERGY
      under Contract DE-AC05-76RL01830

License

HyperNetX is released under the 3-Clause BSD license (see License)