CyNetDiff Submission

[eliotwrobson]

Submitting Author: Name (@eliotwrobson)
All current maintainers: (@eliotwrobson, @abhishekumrawal)
Package Name: CyNetDiff
One-Line Description of Package: A performance-focused library implementing algorithms for simulating network diffusion processes, written in Cython.
Repository Link: https://github.com/eliotwrobson/CyNetDiff
Version submitted: v0.1.13
EIC: @Batalex
Editor: @sneakers-the-rat, @lwasser
Reviewer 1: @Kai-Striega
Reviewer 2: @jeremyzhangsq
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

---

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:

Network diffusion processes aim to model the spread of information through social networks, represented using graphs. Experimental work involving these models usually involves simulating these processes many times over large graphs, which can be computationally very expensive. At the same time, being able to conduct experiments using a high-level language like Python is helpful to researchers, as this gives greater flexibility in developing research software. To address both of these concerns, CyNetDiff is a Cython module implementing the independent cascade and linear threshold models, two of the most popular network diffusion models. Development has been focused on performance, while still giving an intuitive, high-level interface to assist in research tasks.

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

• Geospatial
• Education

Community Partnerships

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

• Astropy:My package adheres to Astropy community standards
• 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?

This is aimed at researchers working in areas related to network diffusion and influence maximization, and specifically at optimizing the most computationally expensive part of this process. This should enable researchers to conduct experiments on larger graphs than would be possible with a pure-Python package. For a recent work doing experiments that fit the use cases of this package, see https://arxiv.org/abs/2207.08937

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

There is a previous package filling a similar use case called ndlib: https://github.com/GiulioRossetti/ndlib
Our package differs as it was developed with a focus on performance, and with lesser emphasis on visualization
and flexibility (for example, we do not have a way of defining custom models). Using code compiled with Cython
allows our package to handle much larger graphs than are possible with a pure-Python package like ndlib.

• 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:

#165, @Batalex

Continuing off of the discussion there, the main goals of the review for me are to finalize the interface and add related features / utility code to aid usability. In terms of concrete features, the algorithms already implemented are enough for a full release.

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI approved license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

Is it possible to add the paper submission after this review is completed? Would like the content of the paper to incorporate any functionality that gets added during the review process.

• Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

• The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
• The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
• The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/.
• The package is deposited in a long-term repository with the DOI:

Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

• Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

• I have read the author guide.
• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

• Last but not least please fill out our pre-review survey. This helps us track
  submission and improve our peer review process. We will also ask our reviewers
  and editors to fill this out.

P.S. Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

The editor template can be found here.

The review template can be found here.

Review issues

• Responses to review comments eliotwrobson/CyNetDiff#22
• Adopt SPEC-7 Standard eliotwrobson/CyNetDiff#21
• Add examples for user facing functions eliotwrobson/CyNetDiff#25
• Add benchmarks eliotwrobson/CyNetDiff#26
• Provide wheels for other platforms eliotwrobson/CyNetDiff#27

Footnotes

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

[Batalex]

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci review. Below are the basic checks that your package needs to pass to begin our review. If some of these are missing, we will ask you to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements below.

• Installation The package can be installed from a community repository such as PyPI (preferred), and/or a community channel on conda (e.g. conda-forge, bioconda).
  • The package imports properly into a standard Python environment import package.
• Fit The package meets criteria for fit and overlap.
• Documentation The package has sufficient online documentation to allow us to evaluate package function and scope without installing the package. This includes:
  • User-facing documentation that overviews how to install and start using the package.
  • Short tutorials that help a user understand how to use the package and what it can do for them.
  • API documentation (documentation for your code's functions, classes, methods and attributes): this includes clearly written docstrings with variables defined using a standard docstring format.
• Core GitHub repository Files
  • README The package has a README.md file with clear explanation of what the package does, instructions on how to install it, and a link to development instructions.
  • Contributing File The package has a CONTRIBUTING.md file that details how to install and contribute to the package.
  • Code of Conduct The package has a CODE_OF_CONDUCT.md file.
  • License The package has an OSI approved license.
    NOTE: We prefer that you have development instructions in your documentation too.
• Issue Submission Documentation All of the information is filled out in the YAML header of the issue (located at the top of the issue template).
• Automated tests Package has a testing suite and is tested via a Continuous Integration service.
• Repository The repository link resolves correctly.
• Package overlap The package doesn't entirely overlap with the functionality of other packages that have already been submitted to pyOpenSci.
• Archive (JOSS only, may be post-review): The repository DOI resolves correctly.
• Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?

---

• 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. 🙌

---

---

Editor comments

I think the documentation is a little bare currently to get started with the review. I'd like to see the information you wrote in this submission issue in the project repository itself. For example:

Network diffusion processes aim to model the spread of information through social networks, represented using graphs. Experimental work involving these models usually involves simulating these processes many times over large graphs, which can be computationally very expensive. At the same time, being able to conduct experiments using a high-level language like Python is helpful to researchers, as this gives greater flexibility in developing research software. To address both of these concerns, CyNetDiff is a Cython module implementing the independent cascade and linear threshold models, two of the most popular network diffusion models. Development has been focused on performance, while still giving an intuitive, high-level interface to assist in research tasks.

This is a nice elevator pitch to rework and include in the README.

I'd like to see more content in the documentation as well. The API section is great, but the basic example lacks context and does not really show case what CyNetDiff is great for. I want to share with you this resource: Diátaxis framework.
I am not asking that you follow the full framework to the letter, and it would be a huge task to do so, but I think there is some virtue in thinking about the kind of documentation you aim to showcase.
The documentation is the first thing a potential user will take a look at. As such, it needs to quickly guide the reader to:

• what does it do? (= should I try it?)
• how does it work? (= ok, I think this is the tool I need, how do I proceed?)

[eliotwrobson]

Thank you for the feedback! I think those changes are reasonable and will significantly enhance the package, I'll try and expand the documentation by Monday so we can get the ball rolling on the review before too long 🚀

[eliotwrobson]

@Batalex I have just published a release with the following changes:

• Updated the elevator pitch and example in the README
• Added two longer extended examples to the documentation site

[Batalex]

Thanks, this looks great. From what I can tell, we are good to go. I'll get started on finding the perfect editor for this review 🐈‍⬛

[eliotwrobson]

@Batalex I know we don't have an editor yet, but is it too early to begin suggesting reviewers? We've already found someone that might be a good candidate for this.

[Batalex]

@Batalex I know we don't have an editor yet, but is it too early to begin suggesting reviewers? We've already found someone that might be a good candidate for this.

That's fantastic!
Could you please orient them to our signups form? https://forms.gle/GHfxvmS47nQFDcBM6

[eliotwrobson]

For sure! I'll be sure to email this person as well, but this is @jeremyzhangsq

[eliotwrobson]

Shiqi has agreed to review and filled out the form!