Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[PRE REVIEW]: PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs #5634

Closed
editorialbot opened this issue Jul 8, 2023 · 30 comments
Assignees
Labels
Makefile pre-review Shell TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning

Comments

@editorialbot
Copy link
Collaborator

editorialbot commented Jul 8, 2023

Submitting author: @diningphil (Federico Errica)
Repository: https://github.com/diningphil/PyDGN/
Branch with paper.md (empty if default branch): paper
Version: v1.3.1
Editor: @arfon
Reviewers: @idoby, @sepandhaghighi
Managing EiC: George K. Thiruvathukal

Status

status

Status badge code:

HTML: <a href="https://joss.theoj.org/papers/912b1b0ae8bf2ae2d44aaafc50126b74"><img src="https://joss.theoj.org/papers/912b1b0ae8bf2ae2d44aaafc50126b74/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/912b1b0ae8bf2ae2d44aaafc50126b74/status.svg)](https://joss.theoj.org/papers/912b1b0ae8bf2ae2d44aaafc50126b74)

Author instructions

Thanks for submitting your paper to JOSS @diningphil. Currently, there isn't a JOSS editor assigned to your paper.

@diningphil if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

Editor instructions

The JOSS submission bot @editorialbot is here to help you find and assign reviewers and start the main review. To find out what @editorialbot can do for you type:

@editorialbot commands
@editorialbot editorialbot added pre-review Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Jul 8, 2023
@editorialbot
Copy link
Collaborator Author

Hello human, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf

@editorialbot
Copy link
Collaborator Author

Software report:

github.com/AlDanial/cloc v 1.88  T=0.12 s (901.4 files/s, 144621.3 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          47           2020           3800           7061
YAML                            28            336            273           1139
Markdown                         5            282              0            438
reStructuredText                15            242            449            240
TeX                              1             21              0            151
SVG                              3              2              0             89
Bourne Shell                     2             20             15             47
DOS Batch                        1              8              1             26
make                             1              4              7              9
TOML                             1              0              0              6
-------------------------------------------------------------------------------
SUM:                           104           2935           4545           9206
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

@editorialbot
Copy link
Collaborator Author

Wordcount for paper.md is 1122

@gkthiruvathukal
Copy link

@editorialbot generate pdf

@editorialbot
Copy link
Collaborator Author

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- 10.1109/72.572108 may be a valid DOI for title: Supervised neural networks for the classification of structures
- 10.1109/tnn.2008.2010350 may be a valid DOI for title: Neural network for graphs: A contextual constructive approach
- 10.1109/msp.2017.2693418 may be a valid DOI for title: Geometric deep learning: going beyond Euclidean data

INVALID DOIs

- None

@editorialbot
Copy link
Collaborator Author

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

1 similar comment
@editorialbot
Copy link
Collaborator Author

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

@gkthiruvathukal
Copy link

@editorialbot invite @jni as editor

@editorialbot
Copy link
Collaborator Author

Invitation to edit this submission sent!

@diningphil
Copy link

diningphil commented Jul 8, 2023

List of potential reviewers working on graphs and AI:

  • idoby
  • jkbren
  • nidhog

@diningphil
Copy link

@editorialbot commands

@editorialbot
Copy link
Collaborator Author

Hello @diningphil, here are the things you can ask me to do:


# List all available commands
@editorialbot commands

# Get a list of all editors's GitHub handles
@editorialbot list editors

# Check the references of the paper for missing DOIs
@editorialbot check references

# Perform checks on the repository
@editorialbot check repository

# Adds a checklist for the reviewer using this command
@editorialbot generate my checklist

# Set a value for branch
@editorialbot set joss-paper as branch

# Generates the pdf paper
@editorialbot generate pdf

# Generates a LaTeX preprint file
@editorialbot generate preprint

# Get a link to the complete list of reviewers
@editorialbot list reviewers

@jni
Copy link

jni commented Jul 8, 2023

Apologies @gkthiruvathukal, I am travelling ~non-stop till October so I really don't have the bandwidth right now and must decline. 🙏

@Kevin-Mattheus-Moerman
Copy link
Member

@editorialbot invite @adonath as editor

@editorialbot
Copy link
Collaborator Author

Invitation to edit this submission sent!

@diningphil
Copy link

diningphil commented Jul 19, 2023

Hi @Kevin-Mattheus-Moerman,

I believe @adonath is on parental leave (judging from his status on Github). Do you think it would be good to invite another editor for the submission?

Many thanks,
Federico

@arfon
Copy link
Member

arfon commented Jul 23, 2023

@editorialbot assign me as editor

@editorialbot
Copy link
Collaborator Author

Assigned! @arfon is now the editor

@arfon
Copy link
Member

arfon commented Jul 23, 2023

👋 @idoby @jkbren @nidhog would any of you be willing to review this submission for JOSS? The submission under consideration is PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. You can learn more about the process in these guidelines: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html

Based on your experience, we think you might be able to provide a great review of this submission. Please let me know if you think you can help us out!

Many thanks
Arfon

@idoby
Copy link

idoby commented Jul 23, 2023

Sure, this seems interesting and very relevant.
Thanks for considering me!

@arfon
Copy link
Member

arfon commented Jul 24, 2023

Fantastic, thanks @idoby! I'll go ahead and add you as a reviewer now but won't actually start the review thread until I have a second reviewer lined up. Hopefully this won't be too long.

@arfon
Copy link
Member

arfon commented Jul 24, 2023

@editorialbot add @idoby as reviewer

@editorialbot
Copy link
Collaborator Author

@idoby added to the reviewers list!

@arfon
Copy link
Member

arfon commented Jul 31, 2023

👋 @sepandhaghighi @matteocao – would either of you be willing to review this submission for JOSS? The submission under consideration is PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs.

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. You can learn more about the process in these guidelines: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html

Based on your experience, we think you might be able to provide a great review of this submission. Please let me know if you think you can help us out!

Many thanks
Arfon

@sepandhaghighi
Copy link

👋 @sepandhaghighi @matteocao – would either of you be willing to review this submission for JOSS?

Hi
Why not?
I'm available.

SH

@arfon
Copy link
Member

arfon commented Aug 1, 2023

@editorialbot add @sepandhaghighi as reviewer

Thanks @sepandhaghighi!

@editorialbot
Copy link
Collaborator Author

@sepandhaghighi added to the reviewers list!

@arfon
Copy link
Member

arfon commented Aug 1, 2023

@editorialbot start review

@editorialbot
Copy link
Collaborator Author

OK, I've started the review over in #5713.

@arfon
Copy link
Member

arfon commented Aug 1, 2023

@idoby, @sepandhaghighi – see you over in #5713 where the review will actually take place.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
Makefile pre-review Shell TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning
Projects
None yet
Development

No branches or pull requests

8 participants