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[REVIEW]: Raphtory: The temporal graph engine for Rust and Python #5940
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Review checklist for @arashbmConflict of interest
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Review checklist for @abhishektiwariConflict of interest
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@narnolddd please see initial feedback. General checksContribution and authorship— I was unable to find GitHub commits from Matt Barnes on Raphtory. Can you please comment on Matt's contribution for this paper and software? DocumentationInstallation instructionsPlease add guidance for Python and Rust version supported. Also specify version requirements for Example usageA tried many of the examples, and they seem to be outdated and broken (1). For instance, due to unclear guidance on what Python versions are supported, I started with Software paperQuality of writingSmall corrections, — State of fieldI am curious how Raphtory compares with graph databases such as arangodb/neo4j/MemGraph? I can see comparison benchmark data and scripts providing data points against MemGraph but no mention in the paper itself how Raphtory stack up against graph databases. |
Just a quick feedback on the paper: Great work, smooth installation, and enjoyable use. The manuscript is concise and checks all the right boxes. My only gripe is with the examples.
(Just to throw my unasked-for opinion in the mix, I don't believe that the manuscript would be the best place to publish benchmark results or even extensive comparison with other software, as these are often rapidly changing and highly sensitive to methodology and environment, thus not very reproducible. It's fine if there are benchmarks on the website, where they can be updated frequently but the paper might not be the best venue. Comparison to other software in the manuscript is better limited to differences in the general approach or difference in goals or stating their particular fields of specialisation.) |
@editorialbot generate pdf |
@narnolddd I don't seem to be able to run |
Hi @arashbm , so sorry I should have added a comment earlier, when I regenerated the pdf it was because I was struggling a bit with the package to do it locally (have managed to do it now) and wasn't ready to read. Hope I didn't waste too much of your time with this! The code referenced in the example is at the moment just on a branch which is going to be merged today and will be put into the next release. I also have a reply to both reviews which I will share once that's in. Apologies again for this and thank you for all your time reading/testing the code and the feedback you've given already. |
Raphtory review responseHi @arashbm @abhishektiwari Happy New Year and thank you very much for your time and feedback on the paper. Apologies for the delay in getting back to you, there were a few other changes (unrelated to this submission) that needed to be made/fixed in the repository before we could push a release. This has now been done (Raphtory 0.7.0) as well as the requested changes to the manuscript and README. We have addressed the comments point by point below, but in summary have:
Response to comments from Abhishek
Matt may not have committed directly to Raphtory but has been working on some of the academic/research use cases for the software and his input has been crucial for the design of new features (as is the case for some other authors on the paper).
Thanks, we have now added the Python and Rust version requirement in the README (>=3.8 and >=1.75 respectively).
Apologies for this, they had become out of date and should have been deleted as we had got new versions working elsewhere. We now have one self-contained and comprehensive example (Sociopatterns baboon dataset) for Python.
Thank you for spotting those typos, these have now been fixed.
This is definitely something would like to address in a longer-form paper to give it an in-depth treatment, both due to the space resrictions of the JOSS format and because JOSS' scope seemed to be more focused on highlighting features and general usefulness than performance testing. Response to comments from Arash
Thanks for your feedback. We have tried to make them a little more contained -- they are not entirely independent but assuming the user is familiar with Pandas and Matplotlib it should be clearer now where things are coming from:
Thanks, we agree. We have tried to adjust the spacing a bit to make the fonts larger and allow for some gaps between logical sections of the code (balanced against keping the examples self-contained). |
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Thanks! I'm quite happy with the manuscript and the revisions completely resolve the minor issues I had with the previous version. Great work! |
@narnolddd Revised manuscript and updates look great. Look forward to Raphtory being used both in academia and industry. Thanks to everyone who has work on the software and paper. @luizirber Review checklist is now complete from my side. Please let me know if anything else needed. |
Awesome, thanks @arashbm and @abhishektiwari for the fantastic reviews! |
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👋 @openjournals/dsais-eics, this paper is ready to be accepted and published. Check final proof 👉📄 Download article If the paper PDF and the deposit XML files look good in openjournals/joss-papers#5178, then you can now move forward with accepting the submission by compiling again with the command |
@narnolddd – could you please merge this PR? Pometry/Raphtory#1551 |
Thanks @arfon , merged! |
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Yay, thank you @arfon and @luizirber very much! And thanks again to @arashbm and @abhishektiwari for the really helpful reviews! 🥳 |
@abhishektiwari, @arashbm – many thanks for your reviews here and to @luizirber for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨ @narnolddd – your paper is now accepted and published in JOSS ⚡🚀💥 |
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Submitting author: @narnolddd (Naomi Arnold)
Repository: https://github.com/Pometry/Raphtory
Branch with paper.md (empty if default branch): paper
Version: 0.7.0
Editor: @luizirber
Reviewers: @abhishektiwari, @arashbm
Archive: 10.5281/zenodo.10530613
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✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨
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