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minor changes in paper/bib #150

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minor changes in paper.md
danielskatz Mar 5, 2024
5ced411
minor changes in paper.bib
danielskatz Mar 5, 2024
a95c8c8
fixing my typo
danielskatz Mar 5, 2024
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6 changes: 3 additions & 3 deletions HNX_Paper/paper.bib
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,7 @@ @article{Pickard2023HAT
}

@misc{Berge1973Graphs,
title={Graphs and {H}ypergraphs,(translated by {E}dward {M}inieka)},
title={Graphs and {H}ypergraphs, (translated by {E}dward {M}inieka)},
doi={10.1016/s0924-6509(09)x7013-3},
author={Berge, Claude},
year={1973},
Expand Down Expand Up @@ -203,7 +203,7 @@ @article{Landry2023
number = {85},
pages = {5162},
author = {Nicholas W. Landry and Maxime Lucas and Iacopo Iacopini and Giovanni Petri and Alice Schwarze and Alice Patania and Leo Torres},
title = {XGI: A Python package for higher-order interaction networks}, journal = {Journal of Open Source Software}
title = {XGI: A {P}ython package for higher-order interaction networks}, journal = {Journal of Open Source Software}
}

@inproceedings{Szufel2019,
Expand Down Expand Up @@ -258,4 +258,4 @@ @InProceedings{SciPyProceedings_11
address = {Pasadena, CA USA},
year = {2008},
editor = {Ga\"el Varoquaux and Travis Vaught and Jarrod Millman},
}
}
20 changes: 10 additions & 10 deletions HNX_Paper/paper.md
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Expand Up @@ -57,10 +57,10 @@ Metadata may be attached to the tool by providing tabular data via two optional

# Statement of need
For more than a century, graph theory has provided powerful methods for studying network relationships among abstract entities.
Since the early 2000's, software packages such as NetworkX [@SciPyProceedings_11] and `igraph` [@csardi2006igraph;@antonov2023igraph] have
Since the early 2000s, software packages such as NetworkX [@SciPyProceedings_11] and `igraph` [@csardi2006igraph;@antonov2023igraph] have
made these theoretical tools available to data scientists for studying large data sets.
Graphs represent pairwise interactions between entities, but for many network datasets this is a severe limitation.
In 1973, hypergraphs were introduced by Claude Berge [@Berge1973Graphs] as a strict generalization of graphs: a hyperedge in a hypergraph can contain any number of nodes, including 1, 2, or more.
In 1973, hypergraphs were introduced by @Berge1973Graphs as a strict generalization of graphs: a hyperedge in a hypergraph can contain any number of nodes, including 1, 2, or more.
Hypergraphs have been used to model complex network datasets in
areas such as the biological sciences, information systems, the power grid, and cyber security.
Hypergraphs strictly generalize graphs (all graphs are (2-uniform) hypergraphs), and thus can represent additional data complexity and have more mathematical properties to exploit (for example, hyperedges can be contained in other hyperedges). As mathematical set systems, simplicial and homological methods from
Expand All @@ -71,21 +71,21 @@ 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.
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.
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]
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]
as well as general hypergraph analytics [@Lotito2023Hypergraphx],
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Trailing comma should be a full stop

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sorry, typo

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,
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)
under the HyperNetX visualization (left): 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
Expand All @@ -110,7 +110,7 @@ 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 @@ -121,6 +121,6 @@ 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].
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