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Expand Up @@ -20,39 +20,39 @@ authors:
orcid: 0000-0003-2466-199X
affiliation: "1"
- name: Mark Bonicillo
affiliation: "1"
affiliation: "1"
- name: Cliff Joslyn
orcid: 0000-0002-5923-5547
affiliation: "1"
- name: Emilie Purvine
orcid: 0000-0003-2069-5594
affiliation: "1"
affiliation: "1"
- name: Madelyn Shapiro
orcid: 0000-0002-2786-7056
affiliation: "1"
affiliation: "1"
- name: Ji Young Yun
affiliation: "1"
affiliations:
- name: Pacific Northwest National Laboratory, USA
index: 1

date: 21 June 2023
bibliography: paper.bib

---

# Summary
HyperNetX (HNX) is an open source Python library for the analysis and visualization of complex network data modeled as hypergraphs.
Initially released in 2019, HNX facilitates exploratory data analysis of complex networks using algebraic topology, combinatorics, and generalized hypergraph and graph theoretical methods on structured data inputs.
With its 2023 release, the library supports attaching metadata, numerical and categorical, to nodes (vertices) and hyperedges, as well as to node-hyperedge pairings (incidences).
HNX has a customizable Matplotlib-based visualization module as well as HypernetX-Widget, its JavaScript addon for interactive exploration and visualization of hypergraphs within Jupyter Notebooks. Both packages are available on GitHub and PyPI. With a growing community of users and collaborators, HNX has become a preeminent tool for hypergraph analysis.
HNX has a customizable Matplotlib-based visualization module as well as HypernetX-Widget, its JavaScript addon for interactive exploration and visualization of hypergraphs within Jupyter Notebooks. Both packages are available on GitHub and PyPI. With a growing community of users and collaborators, HNX has become a preeminent tool for hypergraph analysis.

![HNX-Widget is an add-on for the Jupyter Notebook
computational environment, enabling users to view and interactively
explore hypergraphs.
The main features of the tool are: 1) adjustable layout 2) advanced
selection and 3) visual encoding of node and edge properties.
Metadata may be attached to the tool by providing tabular data via two optional data frames indexed by node and hyperedge identifiers. Above is an HNX-Widget visualization of a Scene to Character mapping from the LesMis dataset [@knuth1993].](Figures/hnxexample.png){height="225pt"}
Metadata may be attached to the tool by providing tabular data via two optional data frames indexed by node and hyperedge identifiers. Above is an HNX-Widget visualization of a Scene to Character mapping from the LesMis dataset [@knuth1993].](Figures/hnxexample.png){height="225pt"}


# Statement of need
Expand All @@ -69,24 +69,24 @@ With the development of hypergraph modeling methods, new software was required t
experimentation and exploration, which prompted the development of HyperNetX.

## Related Software
Due to the diversity of hypergraph modeling applications, hypergraph software libraries are
often bootstrapped using data structures and methods most appropriate to their usage.
In 2020 SimpleHypergraph.jl was made available for high performance computing on hypergraphs using Julia.
The library offers a suite of tools for centrality analysis and community detection and integrates its own
visualization tools with those offered by HNX [@Szufel2019]. In 2021 CompleX Group Interactions (XGI) was released.
Originally developed to efficiently discover spreading processes in complex social systems, the library now offers
a statistics package as well as a full suite of hypergraph analysis and visualization tools[@Landry2023].
More recently, in 2023 HyperGraphX (HGX) was released, again with a full suite of tools for community detection
Due to the diversity of hypergraph modeling applications, hypergraph software libraries are
often bootstrapped using data structures and methods most appropriate to their usage.
In 2020 SimpleHypergraph.jl was made available for high performance computing on hypergraphs using Julia.
The library offers a suite of tools for centrality analysis and community detection and integrates its own
visualization tools with those offered by HNX [@Szufel2019]. In 2021 CompleX Group Interactions (XGI) was released.
Originally developed to efficiently discover spreading processes in complex social systems, the library now offers
a statistics package as well as a full suite of hypergraph analysis and visualization tools [@Landry2023].
More recently, in 2023 HyperGraphX (HGX) was released, again with a full suite of tools for community detection
as well as general hypergraph analytics [@Lotito2023Hypergraphx].
A nice compendium of many of the hypergraph libraries created in the last decade can be found in @Kurte2021.

HNX leads the effort to share library capabilities by specifying a Hypergraph Interchange Format (HIF)
for storing hypergraph data as a JSON object. Since hypergraphs can store metadata on its nodes,
HNX leads the effort to share library capabilities by specifying a Hypergraph Interchange Format (HIF)
for storing hypergraph data as a JSON object. Since hypergraphs can store metadata on its nodes,
hyperedges, and incidence pairs, a standardized format makes it easy to share hypergraphs across libraries.

![Visualizations from hypergraph libraries based on the bipartite graph seen in grey
under the HyperNetX visualization (left side): XGI (Center), @Landry2023 and SimpleHypergraph (Right), @Szufel2019.](Figures/3graphs.png)

# Overview of HNX
HNX serves as a platform for the collaboration and sharing of hypergraph
methods within the research community.
Expand All @@ -105,12 +105,12 @@ in the simplicial complexes generated by modestly sized hypergraphs.
These objects, which are used for defining the *Homology Groups*
studied by Algebraic Topologists, offer new metrics for exploratory
data science.

As a collaborative platform, HNX contains contributed modules
and tutorials in the form of Jupyter notebooks
for Laplacian clustering, clustering and modularity, synthetic
generation of hypergraphs, and Contagion Theory.
In its latest release, HNX 2.0 uses Pandas dataframes[@reback2020pandas;@mckinney-proc-scipy-2010] as its underlying data structure,
In its latest release, HNX 2.0 uses Pandas dataframes [@reback2020pandas;@mckinney-proc-scipy-2010] as its underlying data structure,
making the nodes and hyperedges of a hypergraph as accessible as the
cells in a dataframe.
This simple design allows HNX to import data from semantically
Expand All @@ -119,8 +119,8 @@ in order to model and explore their higher order relationships.
Because it is open source, HNX provides a unique opportunity for
hypergraph researchers to implement their own methods built from
HNX and contribute them as modules and Jupyter tutorials to the HNX user community.

## Projects using HNX
HNX was created by the Pacific Northwest National Laboratory. It has provided data analysis and visualization support for academic papers in subject areas such as biological systems [@Feng2021;@Colby2023], cyber security [@Joslyn2020DNS], information systems [@Molnar2022Application], neural networks [@Praggastis2022SVD], knowledge graphs [@joslyn2018], and the foundations of hypergraph theory [@Vazquez2022Growth].
# References

# References

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