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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Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

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.

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

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.

  • If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

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Footnotes

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]

Editor in Chief checks

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• Initial onboarding survey was filled out
  We appreciate each maintainer of the package filling out this survey individually. 🙌
  Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. 🙌

---

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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.

[SimonMolinsky]

Hi, we have our second reviewer! Welcome @Reckony !

[SimonMolinsky]

@JonnyTran , @Reckony

Please confirm your involvement here, and copy & paste the review template from here: TEMPLATE into your responses. For now, it could be empty, you can go through it point by point and check empty boxes, or you can fill it when you end your review.

The review steps are described here: Review Steps. If you feel that something needs to be clarified, I'll guide you!

[JonnyTran]

Package Review (in progress; ETA 08/25/2023)

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• Installation instructions: for the development version of the package and any non-standard dependencies in README.
• Vignette(s) demonstrating major functionality that runs successfully locally.
• Function Documentation: for all user-facing functions.
• Examples for all user-facing functions.
• Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

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The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
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NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• Short description of package goals.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
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Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
Package structure should follow general community best-practices. In general please consider whether:

• Package documentation is clear and easy to find and use.
• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests:
  • All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• A short summary describing the high-level functionality of the software
• Authors: A list of authors with their affiliations
• A statement of need clearly stating problems the software is designed to solve and its target audience.
• References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 2

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Review Comments

1. I really love the extensive and high-quality documentation, vignettes and tutorials. It would be great if the notebooks at /main/tutorials/ can also be rendered on https://xgi.readthedocs.io (see link), to make it more reachable for users. This would also let you order the tutorial .ipynb's instead of being sorted by file name if viewed on GitHub.
2. Regarding plotting XGI's hypergraphs, the functions to generate and customize matplotlib plots seems very purposed and useful. If a user wanted to use another plotting tool (e.g. bokeh, plotly), it would be easier for them to work with XGI if there are functions to export nodes & edges data as a Pandas dataframe, and ideally return two dataframes for the nodes and the edges.
3. If XGI's API is designed to be similar to NetworkX's, would users expect NetworkX's functions are compatible with XGI's Hypergraph's?

[Reckony]

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

• As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

• A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• Installation instructions: for the development version of the package and any non-standard dependencies in README.
• Vignette(s) demonstrating major functionality that runs successfully locally.
• Function Documentation: for all user-facing functions.
• Examples for all user-facing functions.
• Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements
The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• Short description of package goals.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
Package structure should follow general community best-practices. In general please consider whether:

• Package documentation is clear and easy to find and use.
• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests:
  • All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
  • Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

• The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• A short summary describing the high-level functionality of the software
• Authors: A list of authors with their affiliations
• A statement of need clearly stating problems the software is designed to solve and its target audience.
• References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

• The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

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Review Comments

xgi-org/xgi#453

• README does not say if there are other similar packages and how is it different from them - unless it is not applicable?
• I understand this package is not applying to JOSS?

[JonnyTran]

Hi all, just letting you know I'm still working my review, but have a few job interviews coming up that filled up my calendar. Would it be okay with @nwlandry if I finish my review by Aug 18, or would you prefer I drag it closer?

[nwlandry]

Hi @JonnyTran! Thanks for reaching out! From our end, there is no rush, so August 18th is fine. Best of luck with the interviews!

[SimonMolinsky]

Hi @Reckony

Do you need any help or guidance with the review? I'm here to help you. We can even make a call and discuss it :)

[Reckony]

Hi folks, sorry for delay in reviewing, I am in work marathon at the moment due to important milestones… Nevertheless I am working on it to deliver as said 👌

[Reckony]

Hi, could you please refer to my review questions at the bottom of review checklist? I'm almost done :)

[JonnyTran]

@nwlandry I am curious if you have seen comments from @JonnyTran - could you elaborate here?

@JonnyTran - do those comments block the package from the acceptance? Or are those propositions for future development of the package?

I meant to comment as suggestions for future development and none are blocking.
But for comment 2, I can add an issue for xgi.to_bipartite_pandas_dataframe(H)

[JonnyTran]

@maximelucas

@JonnyTran's questions:
2. That's a good idea, we'll look into that. Right now it's possible to have a {edge_id: node_members} and {node_id: edge_memberships} dictionaries with H.edges.members(dtype=dict) and H.nodes.memberships(). Is that what you were thinking, but in a pandas dataframe?

Actually I misunderstood about xgi.to_bipartite_pandas_dataframe(H) so please nvm about that. I meant to suggest that XGI can have functions similar to nx.to_pandas_edgelist() to export to dataframes, which is more conducive for working with other plotting libraries.

3. I never thought of that. I would've thought that users wouldn't think they could use networkx functions. Are you thinking of a place in particular in the documentation that might be confusing regarding this, or a way to make sure it's clear? In any case it's not designed so close so as to be able to use networkx functions, but a few might incidentally work, we can check this.

I couldn't find any particular documentation suggest compatibility, but in using an API that's similar to NetworkX's, I think some users might try the familiar method nx.neighbors(H, n_id) instead of H.nodes.neighbors(n_id). I think it is more helpful to make it clear that XGI is not meant to be an extension of NetworkX to avoid these trials-and-errors.

[SimonMolinsky]

I've seen that you pushed a PR with the requested changes in README.md (badges). @nwlandry , @maximelucas could you let us know when the PR is accepted?

[nwlandry]

Hi @SimonMolinsky! So sorry for my delayed reply on this. I will certainly let you know when the PR is merged. I have looked through all the comments and suggestions and in response, we are doing the following:

• Immediate changes with PR #470.
  • Adding badges for python versions and the project's status (thanks @Reckony!)
  • Adding a copyable citation bibtex entry for easy citation (thanks Simon!)
  • Adding a more helpful description of what XGI actually does (thanks @Reckony!)
• Long-term changes with Issue #472 and PR #457 (Thanks @JonnyTran and Simon!!).
  • Integrating the tutorials into our website will be an extensive effort, but we are committed to continuing this work.

Because of scheduling constraints, I anticipate that we will merge the PR addressing the immediate changes sometime next week.

[nwlandry]

Hi @SimonMolinsky! PR #470 is now merged and I published a new XGI version on PyPI to reflect this. Thanks so much.

[SimonMolinsky]

@Reckony , @JonnyTran Could you look into the newest changes and update your reviews accordingly? We are close to the package acceptance :)

[Reckony]

Final review done, no more issues from my side. Thanks

[JonnyTran]

Everything looks great to me, and I approve the Final approval (post-review)!

[SimonMolinsky]

🎉 XGI has been approved by pyOpenSci! Thank you @nwlandry for submitting XGI and many thanks to @Reckony and @JonnyTran for reviewing this package! 😸

Author Wrap Up Tasks

@nwlandry there are two tasks for you to perform:

• Activate Zenodo watching the repo if you haven't already done so.
• Tag and create a release to create a Zenodo version and DOI.
• Add the badge for pyOpenSci peer-review to the README.md of XGI. The badge should be [![pyOpenSci](https://tinyurl.com/y22nb8up)](https://github.com/pyOpenSci/software-review/issues/115).
• Please fill out the post-review survey. All maintainers and reviewers should fill this out.

It looks like you would like to submit this package to JOSS. Here are the next steps:

• Once the JOSS issue is opened for the package, we strongly suggest that you subscribe to issue updates. This will allow you to continue to update the issue labels on this review as it goes through the JOSS process.
• Login to the JOSS website and fill out the JOSS submission form using your Zenodo DOI. When you fill out the form, be sure to mention and link to the approved pyOpenSci review. JOSS will tag your package for expedited review if it is already pyOpenSci approved.
• Wait for a JOSS editor to approve the presubmission (which includes a scope check).
• Once the package is approved by JOSS, you will be given instructions by JOSS about updating the citation information in your README file.
• When the JOSS review is complete, add a comment to your review in the pyOpenSci software-review repo here that it has been approved by JOSS. An editor will then add the JOSS-approved label to this issue.

🎉 Congratulations! You are now published with both JOSS and pyOpenSci! 🎉

---

If you have any feedback for us about the review process please feel free to share it here. We are always looking to improve our process and documentation in the peer-review-guide.

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@JonnyTran and @Reckony this survey post-review survey is designed for reviewers too, so please fill it out when you're ready!

(1) @nwlandry I know that your package is published in JOSS, so I've checked a few boxes for you.

(2) @nwlandry if you want to, you can submit a blog post highlighting XGI, mostly for promotional purposes. Here's an example in markdown.

[SimonMolinsky]

Editor Final Checks

Please complete the final steps to wrap up this review. Editor, please do the following:

• Make sure that the maintainers filled out the post-review survey
• Invite the maintainers to submit a blog post highlighting their package. Feel free to use / adapt language found in this comment to help guide the author.
• Change the status tag of the issue to 6/pyOS-approved6 🚀🚀🚀.
• If the author submits to JOSS, please continue to update the labels for JOSS on this issue until the author is accepted (do not remove the 6/pyOS-approved label). Once accepted add the label 9/joss-approved to the issue. Skip this check if the package is not submitted to JOSS.

[nwlandry]

@SimonMolinsky Thanks! I just completed the survey and added the badge. I will bring up the blog post at our all-team meeting on October 10th.

[SimonMolinsky]

@nwlandry The one last thing is the badge:

Add the badge for pyOpenSci peer-review to the README.md of XGI. The badge should be [![pyOpenSci](https://tinyurl.com/y22nb8up)](https://github.com/pyOpenSci/software-review/issues/115).

Let me know when it's done.

I'm not sure if you or your friends are the part of pyOpenSci Slack community at this moment, but if not, and you want to, then let me know, and I will send invitations for you :)

[nwlandry]

Hi @SimonMolinsky, thanks for the reminder! Should be done now. I would love an invitation to the Slack and I will ask the rest of the XGI team as well. Thanks @JonnyTran and @Reckony for your helpful reviews!

[SimonMolinsky]

@nwlandry I'll close the issue soon (at the end of this week) because XGI is ready to be a part of pyOpenSci. We are honored that you have submitted your work here!

About the blog post: we can communicate here or by email. I will email you about joining the pyOpenSci community.

@Reckony & @JonnyTran Thank you for your support! 🙌