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

[editorialbot]

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

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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]

Wordcount for paper.md is 1122

[gkthiruvathukal]

@editorialbot generate pdf

[editorialbot]

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]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[gkthiruvathukal]

@editorialbot invite @jni as editor

[editorialbot]

Invitation to edit this submission sent!

[diningphil]

List of potential reviewers working on graphs and AI:

• idoby
• jkbren
• nidhog

[diningphil]

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[jni]

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]

@editorialbot invite @adonath as editor

[editorialbot]

Invitation to edit this submission sent!

[diningphil]

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]

@editorialbot assign me as editor

[editorialbot]

Assigned! @arfon is now the editor

[arfon]

:wave: @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]

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

[arfon]

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]

@editorialbot add @idoby as reviewer

[editorialbot]

@idoby added to the reviewers list!

[arfon]

:wave: @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]

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

Hi Why not? I'm available.

SH

[arfon]

@editorialbot add @sepandhaghighi as reviewer

Thanks @sepandhaghighi!

[editorialbot]

@sepandhaghighi added to the reviewers list!

[arfon]

@editorialbot start review

[editorialbot]

OK, I've started the review over in https://github.com/openjournals/joss-reviews/issues/5713.

[arfon]

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