XGI

[nwlandry]

Submitting Author: (@nwlandry)
All current maintainers: (@nwlandry, @leotrs, @maximelucas, @iaciac, @lordgrilo, @acuschwarze, @thomasrobiglio, @alpatania)
Package Name: XGI
One-Line Description of Package: XGI is a Python package for higher-order networks.
Repository Link: https://github.com/xgi-org/xgi
Version submitted: 0.7
Editor: Szymon Moliński (@SimonMolinsky)
Reviewer 1: Nhat (Jonny) Tran (@JonnyTran)
Reviewer 2: Marta Leszczyńska (@Reckony)
Archive:
Version accepted: 0.7.4
JOSS DOI:
Date accepted (month/day/year): 09/23/2023

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Description

• Include a brief paragraph describing what your package does: CompleX Group Interactions (XGI) is a library for analyzing higher-order networks. Such networks are used to model interactions of arbitrary size between entities in a complex system. This library provides methods for building hypergraphs and simplicial complexes; algorithms to analyze their structure, visualize them, and simulate dynamical processes on them; and a collection of higher-order datasets. XGI is implemented in pure Python and integrates with the rest of the Python scientific stack. XGI is designed and developed by network scientists with the needs of network scientists in mind.

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  • Data retrieval
  • Data extraction
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Domain Specific & Community Partnerships

- [ ] Geospatial
- [ ] Education
- [ ] Pangeo

Community Partnerships

If your package is associated with an
existing community please check below:

• NumFOCUS
  XGI is a NumFOCUS Affiliated Project.

• Pangeo

  • My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

• For all submissions, explain how the and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):

  • Who is the target audience and what are scientific applications of this package?
    The target audience is network scientists across multiple disciplines, particularly those interested in networks that contain higher-order interactions. The scientific application of this package is provide a common language for network scientists to work on projects related to higher-order interactions by providing common data structures, algorithms, and basic visualization tools as well as standardized datasets.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    There are several existing packages to represent and analyze higher-order networks: HyperNetX, Hypergraphx, Hypergraph Analysis Toolbox (HAT), and Reticula in Python, SimpleHypergraphs.jl and HyperGraphs.jl in Julia, and hyperG in R. XGI is a valuable addition to the current suite of library available. First, XGI is implemented in pure Python, ensuring interoperability and easy installation across operating systems. Second, like several of the packages listed, XGI has a well-documented codebase and tutorials designed to make the learning process intuitive. Third, in contrast to existing packages, XGI contains a stats module enabling researchers to easily access established nodal and edge quantities, and even define custom quantities. Fourth, XGI offers data structures for hypergraphs, directed hypergraphs, and simplicial complexes, which allows users to explore a wider range of interaction models than comparable packages. Lastly, XGI integrates higher-order datasets with its interface, providing a standard format in which to store hypergraphs with attributes and a data repository with corresponding functions to load these datasets.

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1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

[NickleDave]

Hi @nwlandry welcome to pyOpenSci!

Just checking: is this an intentional submission?
If so, please know we can't proceed with a review until all the fields in the template above are filled out.

At first glance XGI does appear to be in scope; we can close this and start a new issue later if you're not ready to submit just yet.

[nwlandry]

Hi @NickleDave - yes, it was an intentional submission, but I didn't realize all the missing fields. Sorry about that! I will correct this now. This is already published in JOSS, so I won't fill out the JOSS related items. Thanks so much.

[NickleDave]

Great, thank you!

[nwlandry]

Okay, should be all set I believe. Thanks so much!

[SimonMolinsky]

Hi @nwlandry 👋

I'll go through the submission process with you. Thanks for filling out the survey! Now it's my turn ♟️ I will perform initial checks of the package, and if everything is fine, the review process begins. Please, ping me in cases related to the package or review process.

[SimonMolinsky]

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Editor comments

• JOSS paper citation in the docs (recommended)
• Optional: you may consider rendering tutorials from notebooks to sphinx docs (example)

[SimonMolinsky]

Hi @nwlandry !

I've checked your package, and it is beyond expectations. Every point from the checklist is valid! I've included two comments that may be interesting for you (but both are optional, do as you wish :) ):

1. I didn't find the JOSS paper citation on your package webpage. Having the Citation header on your main page (additionally to the citation in the repo) would be nice. But it's up to you :) Usually, I cite packages by copying text from README or a documentation page.

2. This is 100% optional: you may consider using the nbsphinx plugin to render your tutorials into the documentation webpage. But it could be tedious because you must track changes in your tutorials and/or have these in two places, so I understand if you don't want to do it :)

For now:

I will start looking for the reviewers for your package. It should take around 1 week, hopefully not more :) @nwlandry Could you help me a little with this task and provide ~5 categories that your package falls into? Thanks in advance!

[nwlandry]

Categories that XGI falls into:

• network science
• higher-order networks (hypergraph, simplicial complex)
• data analysis and graph algorithms
• network visualization
• dataset I/O

[nwlandry]

Hope this is what you meant!

[SimonMolinsky]

@nwlandry thank you, this is what I needed!

[SimonMolinsky]

We have the first reviewer on board! Welcome @JonnyTran ! And we are close to assigning the second reviewer, and the package review will start soon.