The HyperNetX library provides classes and methods for the analysis and visualization of complex network data modeled as hypergraphs. The library generalizes traditional graph metrics.

HypernetX was developed by the Pacific Northwest National Laboratory for the Hypernets project as part of its High Performance Data Analytics (HPDA) program. PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830.

  • Principle Developer and Designer: Brenda Praggastis

  • Visualization: Dustin Arendt, Ji Young Yun

  • High Performance Computing: Tony Liu, Andrew Lumsdaine

  • Principal Investigator: Cliff Joslyn

  • Program Manager: Brian Kritzstein

  • Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, François Théberge

The code in this repository is intended to support researchers modeling data as hypergraphs. We have a growing community of users and contributors. Documentation is available at: <https://pnnl.github.io/HyperNetX/>

For questions and comments contact the developers directly at: <hypernetx@pnnl.gov>

New Features of Version 1.0:

  1. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers. The new hypergraphs are given an attribute static=True.

  2. A C++ addon called [NWHy](docs/build/nwhy.html) can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures.

  3. A JavaScript addon called [Hypernetx-Widget](docs/build/widget.html) can be used to interactively inspect hypergraphs in a Jupyter Notebook.

  4. Four new tutorials highlighting the s-centrality metrics, static Hypergraphs, [NWHy](docs/build/nwhy.html), and [Hypernetx-Widget](docs/build/widget.html).

New Features of Version 1.1

  1. Static Hypergraph refactored to improve performance across all methods.

  2. Added modules and tutorials for Contagion Modeling, Community Detection, Clustering, and Hypergraph Generation.

  3. Cell weights for incidence matrices may be added to static hypergraphs on construction.

New Features of Version 1.2

  1. Added module and tutorial for Modularity and Clustering

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.4.3)

3 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for bpraggastis from gravatar.com bpraggastis Avatar for hypernetx from gravatar.com hypernetx Avatar for RyanDanehy from gravatar.com RyanDanehy

Credits

Author: Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn

License

3-Clause BSD license

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hypernetx-1.2.2.tar.gz (83.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hypernetx-1.2.2-py3-none-any.whl (89.1 kB view details)

Uploaded Python 3

File details

Details for the file hypernetx-1.2.2.tar.gz.

File metadata

  • Download URL: hypernetx-1.2.2.tar.gz
  • Upload date:
  • Size: 83.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for hypernetx-1.2.2.tar.gz
Algorithm Hash digest
SHA256 85e9e7987427ebc505a007299229e61f4a091ccf7f04fa367a759f51d00a4993
MD5 6e550ade2fccc4055fa4245693400d9e
BLAKE2b-256 c89b4821ff6278043dc3866c08ffa9299f30e6f8530ed6ff9052f389a3de6224

See more details on using hashes here.

File details

Details for the file hypernetx-1.2.2-py3-none-any.whl.

File metadata

  • Download URL: hypernetx-1.2.2-py3-none-any.whl
  • Upload date:
  • Size: 89.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for hypernetx-1.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 59fb2af496ba2498351ba98dad8289da1e4dddec0888050db287f46fe0b4a27c
MD5 f2debf63a42af377d1289e10cbfa98c2
BLAKE2b-256 86eaeda5b4644b8a5505515992f2f6f4b65433b3ca439fe17b6f13401290f322

See more details on using hashes here.

Release history Release notifications | RSS feed

2.4.3

2 files

2.4.0

2 files

2.3.13

2 files

2.3.10

2 files

2.3.9

2 files

2.3.8

2 files

2.3.7

2 files

2.3.6

2 files

2.3.5

2 files

2.3.4

2 files

2.3.3

2 files

2.3.2

2 files

2.3.1

2 files

2.3.0

2 files

2.2.0

2 files

2.1.4

2 files

2.1.3

2 files

2.1.2

2 files

2.1.1

2 files

2.1.0

2 files

2.0.5

2 files

2.0.4

2 files

2.0.3

2 files

2.0.2

2 files

2.0.1

2 files

2.0.0.post1

2 files

1.2.5

2 files

1.2.4

2 files

1.2.3

2 files

This release

1.2.2 This release

2 files

1.2

2 files

1.1.3

2 files

1.1.2

2 files

1.1.1

2 files

1.0.4

2 files

1.0.3

2 files

1.0.2

2 files

1.0

2 files

0.3.7

2 files

0.3.6

2 files

0.3.5

2 files

0.3.4

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

1 file

0.2.4

1 file

0.1.9

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page