-
Notifications
You must be signed in to change notification settings - Fork 20
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #4636 from openjournals/joss.05713
Merging automatically
- Loading branch information
Showing
4 changed files
with
1,030 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,346 @@ | ||
<?xml version="1.0" encoding="UTF-8"?> | ||
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1" | ||
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" | ||
xmlns:rel="http://www.crossref.org/relations.xsd" | ||
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" | ||
version="5.3.1" | ||
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd"> | ||
<head> | ||
<doi_batch_id>20231001T124152-064289b2f65e7306fb75b358115e3a310c96c586</doi_batch_id> | ||
<timestamp>20231001124152</timestamp> | ||
<depositor> | ||
<depositor_name>JOSS Admin</depositor_name> | ||
<email_address>[email protected]</email_address> | ||
</depositor> | ||
<registrant>The Open Journal</registrant> | ||
</head> | ||
<body> | ||
<journal> | ||
<journal_metadata> | ||
<full_title>Journal of Open Source Software</full_title> | ||
<abbrev_title>JOSS</abbrev_title> | ||
<issn media_type="electronic">2475-9066</issn> | ||
<doi_data> | ||
<doi>10.21105/joss</doi> | ||
<resource>https://joss.theoj.org</resource> | ||
</doi_data> | ||
</journal_metadata> | ||
<journal_issue> | ||
<publication_date media_type="online"> | ||
<month>10</month> | ||
<year>2023</year> | ||
</publication_date> | ||
<journal_volume> | ||
<volume>8</volume> | ||
</journal_volume> | ||
<issue>90</issue> | ||
</journal_issue> | ||
<journal_article publication_type="full_text"> | ||
<titles> | ||
<title>PyDGN: a Python Library for Flexible and Reproducible | ||
Research on Deep Learning for Graphs</title> | ||
</titles> | ||
<contributors> | ||
<person_name sequence="first" contributor_role="author"> | ||
<given_name>Federico</given_name> | ||
<surname>Errica</surname> | ||
<ORCID>https://orcid.org/0000-0001-5181-2904</ORCID> | ||
</person_name> | ||
<person_name sequence="additional" | ||
contributor_role="author"> | ||
<given_name>Davide</given_name> | ||
<surname>Bacciu</surname> | ||
<ORCID>https://orcid.org/0000-0001-5213-2468</ORCID> | ||
</person_name> | ||
<person_name sequence="additional" | ||
contributor_role="author"> | ||
<given_name>Alessio</given_name> | ||
<surname>Micheli</surname> | ||
<ORCID>https://orcid.org/0000-0001-5764-5238</ORCID> | ||
</person_name> | ||
</contributors> | ||
<publication_date> | ||
<month>10</month> | ||
<day>01</day> | ||
<year>2023</year> | ||
</publication_date> | ||
<pages> | ||
<first_page>5713</first_page> | ||
</pages> | ||
<publisher_item> | ||
<identifier id_type="doi">10.21105/joss.05713</identifier> | ||
</publisher_item> | ||
<ai:program name="AccessIndicators"> | ||
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref> | ||
</ai:program> | ||
<rel:program> | ||
<rel:related_item> | ||
<rel:description>Software archive</rel:description> | ||
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.8396373</rel:inter_work_relation> | ||
</rel:related_item> | ||
<rel:related_item> | ||
<rel:description>GitHub review issue</rel:description> | ||
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/5713</rel:inter_work_relation> | ||
</rel:related_item> | ||
</rel:program> | ||
<doi_data> | ||
<doi>10.21105/joss.05713</doi> | ||
<resource>https://joss.theoj.org/papers/10.21105/joss.05713</resource> | ||
<collection property="text-mining"> | ||
<item> | ||
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.05713.pdf</resource> | ||
</item> | ||
</collection> | ||
</doi_data> | ||
<citation_list> | ||
<citation key="1997sperduti_supervised_1997"> | ||
<article_title>Supervised neural networks for the | ||
classification of structures</article_title> | ||
<author>Sperduti</author> | ||
<journal_title>IEEE Transactions on Neural | ||
Networks</journal_title> | ||
<issue>3</issue> | ||
<volume>8</volume> | ||
<doi>10.1109/72.572108</doi> | ||
<cYear>1997</cYear> | ||
<unstructured_citation>Sperduti, A., & Starita, A. | ||
(1997). Supervised neural networks for the classification of structures. | ||
IEEE Transactions on Neural Networks, 8(3), 714–735. | ||
https://doi.org/10.1109/72.572108</unstructured_citation> | ||
</citation> | ||
<citation key="2009scarselli_graph_2009"> | ||
<article_title>The graph neural network | ||
model</article_title> | ||
<author>Scarselli</author> | ||
<journal_title>IEEE Transactions on Neural | ||
Networks</journal_title> | ||
<issue>1</issue> | ||
<volume>20</volume> | ||
<doi>10.1109/TNN.2008.2005605</doi> | ||
<cYear>2009</cYear> | ||
<unstructured_citation>Scarselli, F., Gori, M., Tsoi, A. C., | ||
Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network | ||
model. IEEE Transactions on Neural Networks, 20(1), 61–80. | ||
https://doi.org/10.1109/TNN.2008.2005605</unstructured_citation> | ||
</citation> | ||
<citation key="2009micheli_neural_2009"> | ||
<article_title>Neural network for graphs: A contextual | ||
constructive approach</article_title> | ||
<author>Micheli</author> | ||
<journal_title>IEEE Transactions on Neural | ||
Networks</journal_title> | ||
<issue>3</issue> | ||
<volume>20</volume> | ||
<doi>10.1109/TNN.2008.2010350</doi> | ||
<cYear>2009</cYear> | ||
<unstructured_citation>Micheli, A. (2009). Neural network | ||
for graphs: A contextual constructive approach. IEEE Transactions on | ||
Neural Networks, 20(3), 498–511. | ||
https://doi.org/10.1109/TNN.2008.2010350</unstructured_citation> | ||
</citation> | ||
<citation key="2017bronstein_geometric_2017"> | ||
<article_title>Geometric deep learning: Going beyond | ||
Euclidean data</article_title> | ||
<author>Bronstein</author> | ||
<journal_title>IEEE Signal Processing | ||
Magazine</journal_title> | ||
<issue>4</issue> | ||
<volume>34</volume> | ||
<doi>10.1109/MSP.2017.2693418</doi> | ||
<cYear>2017</cYear> | ||
<unstructured_citation>Bronstein, M. M., Bruna, J., LeCun, | ||
Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: | ||
Going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4), 25. | ||
18–42. https://doi.org/10.1109/MSP.2017.2693418</unstructured_citation> | ||
</citation> | ||
<citation key="2017gilmer_neural_2017"> | ||
<article_title>Neural message passing for quantum | ||
chemistry</article_title> | ||
<author>Gilmer</author> | ||
<journal_title>Proceedings of the 34th International | ||
Conference on Machine Learning (ICML)</journal_title> | ||
<cYear>2017</cYear> | ||
<unstructured_citation>Gilmer, J., Schoenholz, S. S., Riley, | ||
P. F., Vinyals, O., & Dahl, G. E. (2017). Neural message passing for | ||
quantum chemistry. Proceedings of the 34th International Conference on | ||
Machine Learning (ICML), 1263–1272.</unstructured_citation> | ||
</citation> | ||
<citation key="2017hamilton_representation_2017"> | ||
<article_title>Representation learning on graphs: Methods | ||
and applications</article_title> | ||
<author>Hamilton</author> | ||
<journal_title>IEEE Data Engineering | ||
Bulletin</journal_title> | ||
<issue>3</issue> | ||
<volume>40</volume> | ||
<cYear>2017</cYear> | ||
<unstructured_citation>Hamilton, W. L., Ying, R., & | ||
Leskovec, J. (2017). Representation learning on graphs: Methods and | ||
applications. IEEE Data Engineering Bulletin, 40(3), | ||
52–74.</unstructured_citation> | ||
</citation> | ||
<citation key="2018lipton_troubling_2018"> | ||
<article_title>Troubling trends in machine learning | ||
scholarship</article_title> | ||
<author>Lipton</author> | ||
<journal_title>arXiv preprint | ||
arXiv:1807.03341</journal_title> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Lipton, Z. C., & Steinhardt, J. | ||
(2018). Troubling trends in machine learning scholarship. arXiv Preprint | ||
arXiv:1807.03341.</unstructured_citation> | ||
</citation> | ||
<citation key="2018shchur_pitfalls_2018"> | ||
<article_title>Pitfalls of graph neural network | ||
evaluation</article_title> | ||
<author>Shchur</author> | ||
<journal_title>Workshop on Relational Representation | ||
Learning, Neural Information Processing Systems | ||
(NeurIPS)</journal_title> | ||
<cYear>2018</cYear> | ||
<unstructured_citation>Shchur, O., Mumme, M., Bojchevski, | ||
A., & Günnemann, S. (2018). Pitfalls of graph neural network | ||
evaluation. Workshop on Relational Representation Learning, Neural | ||
Information Processing Systems (NeurIPS).</unstructured_citation> | ||
</citation> | ||
<citation key="2019fey_fast_2019"> | ||
<article_title>Fast graph representation learning with | ||
PyTorch Geometric</article_title> | ||
<author>Fey</author> | ||
<journal_title>Representation Learning on Graphs and | ||
Manifolds Workshop, International Conference on Learning Representations | ||
(ICLR)</journal_title> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Fey, M., & Lenssen, J. E. (2019). | ||
Fast graph representation learning with PyTorch Geometric. | ||
Representation Learning on Graphs and Manifolds Workshop, International | ||
Conference on Learning Representations (ICLR).</unstructured_citation> | ||
</citation> | ||
<citation key="2019paszke_pytorch_2019"> | ||
<article_title>Pytorch: An imperative style, | ||
high-performance deep learning library</article_title> | ||
<author>Paszke</author> | ||
<journal_title>Proceedings of the 33rd conference on neural | ||
information processing systems (NeurIPS)</journal_title> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Paszke, A., Gross, S., Massa, F., | ||
Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, | ||
N., Antiga, L., & others. (2019). Pytorch: An imperative style, | ||
high-performance deep learning library. Proceedings of the 33rd | ||
Conference on Neural Information Processing Systems | ||
(NeurIPS).</unstructured_citation> | ||
</citation> | ||
<citation key="2020_you_design_2020"> | ||
<article_title>Design space for graph neural | ||
networks</article_title> | ||
<author>You</author> | ||
<journal_title>Proceedings of the 34th conference on neural | ||
information processing systems (NeurIPS)</journal_title> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>You, J., Ying, Z., & Leskovec, J. | ||
(2020). Design space for graph neural networks. Proceedings of the 34th | ||
Conference on Neural Information Processing Systems | ||
(NeurIPS).</unstructured_citation> | ||
</citation> | ||
<citation key="2019wang_dgl_2019"> | ||
<article_title>Deep graph library: A graph-centric, | ||
highly-performant package for graph neural networks</article_title> | ||
<author>Wang</author> | ||
<journal_title>arXiv preprint | ||
arXiv:1909.01315</journal_title> | ||
<cYear>2019</cYear> | ||
<unstructured_citation>Wang, M., Zheng, D., Ye, Z., Gan, Q., | ||
Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., | ||
Karypis, G., Li, J., & Zhang, Z. (2019). Deep graph library: A | ||
graph-centric, highly-performant package for graph neural networks. | ||
arXiv Preprint arXiv:1909.01315.</unstructured_citation> | ||
</citation> | ||
<citation key="2020bacciu_gentle_2020"> | ||
<article_title>A gentle introduction to deep learning for | ||
graphs</article_title> | ||
<author>Bacciu</author> | ||
<journal_title>Neural Networks</journal_title> | ||
<volume>129</volume> | ||
<doi>10.1016/j.neunet.2020.06.006</doi> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Bacciu, D., Errica, F., Micheli, A., | ||
& Podda, M. (2020). A gentle introduction to deep learning for | ||
graphs. Neural Networks, 129, 203–221. | ||
https://doi.org/10.1016/j.neunet.2020.06.006</unstructured_citation> | ||
</citation> | ||
<citation key="2020errica_fair_2020"> | ||
<article_title>A fair comparison of graph neural networks | ||
for graph classification</article_title> | ||
<author>Errica</author> | ||
<journal_title>8th International Conference on Learning | ||
Representations (ICLR)</journal_title> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Errica, F., Podda, M., Bacciu, D., | ||
& Micheli, A. (2020). A fair comparison of graph neural networks for | ||
graph classification. 8th International Conference on Learning | ||
Representations (ICLR).</unstructured_citation> | ||
</citation> | ||
<citation key="2020hu_open_2020"> | ||
<article_title>Open graph benchmark: Datasets for machine | ||
learning on graphs</article_title> | ||
<author>Hu</author> | ||
<journal_title>Proceedings of the 34th conference on neural | ||
information processing systems (NeurIPS)</journal_title> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Hu, W., Fey, M., Zitnik, M., Dong, | ||
Y., Ren, H., Liu, B., Catasta, M., & Leskovec, J. (2020). Open graph | ||
benchmark: Datasets for machine learning on graphs. Proceedings of the | ||
34th Conference on Neural Information Processing Systems (NeurIPS), | ||
22118–22133.</unstructured_citation> | ||
</citation> | ||
<citation key="2020wu_comprehensive_2020"> | ||
<article_title>A comprehensive survey on graph neural | ||
networks</article_title> | ||
<author>Wu</author> | ||
<journal_title>IEEE Transactions on Neural Networks and | ||
Learning Systems</journal_title> | ||
<doi>10.1109/TNNLS.2020.2978386</doi> | ||
<cYear>2020</cYear> | ||
<unstructured_citation>Wu, Z., Pan, S., Chen, F., Long, G., | ||
Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph | ||
neural networks. IEEE Transactions on Neural Networks and Learning | ||
Systems. | ||
https://doi.org/10.1109/TNNLS.2020.2978386</unstructured_citation> | ||
</citation> | ||
<citation key="2021grattarola_graph_2021"> | ||
<article_title>Graph neural networks in TensorFlow and keras | ||
with spektral</article_title> | ||
<author>Grattarola</author> | ||
<journal_title>IEEE Computational Intelligence | ||
Magazine</journal_title> | ||
<issue>1</issue> | ||
<volume>16</volume> | ||
<doi>10.1109/MCI.2020.3039072</doi> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Grattarola, D., & Alippi, C. | ||
(2021). Graph neural networks in TensorFlow and keras with spektral. | ||
IEEE Computational Intelligence Magazine, 16(1), 99–106. | ||
https://doi.org/10.1109/MCI.2020.3039072</unstructured_citation> | ||
</citation> | ||
<citation key="2021liu_dig_2021"> | ||
<article_title>DIG: A turnkey library for diving into graph | ||
deep learning research</article_title> | ||
<author>Liu</author> | ||
<journal_title>Journal of Machine Learning | ||
Research</journal_title> | ||
<issue>240</issue> | ||
<volume>22</volume> | ||
<cYear>2021</cYear> | ||
<unstructured_citation>Liu, M., Luo, Y., Wang, L., Xie, Y., | ||
Yuan, H., Gui, S., Yu, H., Xu, Z., Zhang, J., Liu, Y., Yan, K., Liu, H., | ||
Fu, C., Oztekin, B. M., Zhang, X., & Ji, S. (2021). DIG: A turnkey | ||
library for diving into graph deep learning research. Journal of Machine | ||
Learning Research, 22(240), 1–9.</unstructured_citation> | ||
</citation> | ||
</citation_list> | ||
</journal_article> | ||
</journal> | ||
</body> | ||
</doi_batch> |
Oops, something went wrong.