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: 10.5281/zenodo.10801503
Version accepted: v0.1.18
Date accepted (month/day/year): 08/22/2025

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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.

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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

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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.

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#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.

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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

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[Batalex]

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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!