Merge pull request #128 from ReScience/2022_41

[2022] Paper 41
ReScience · Jul 20, 2023 · aa93c17 · aa93c17
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diff --git a/2022/41/Makefile b/2022/41/Makefile
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+../template/Makefile
diff --git a/2022/41/README.md b/2022/41/README.md
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+### [ReScience C](https://rescience.github.io/) article template
+
+This repository contains the Latex (optional) template for writing a ReScience
+C article and the (mandatory) YAML metadata file. For the actual article,
+you're free to use any software you like as long as you enforce the proposed
+PDF style. A tool is available for the latex template that produces latex
+definitions from the metadata file. If you use another software, make sure that
+metadata and PDF are always synced.
+
+You can also use overleaf with the [provided template](https://www.overleaf.com/read/kfrwdmygjyqw) but in this case, you'll have to enter `metadata.tex` manually.
+
+#### Usage
+
+For a submission, fill in information in
+[metadata.yaml](./metadata.yaml), modify [content.tex](content.tex)
+and type:
+
+```bash
+$ make 
+```
+
+This will produce an `article.pdf` using xelatex and provided font.
+
+
+After acceptance, you'll need to complete [metadata.yaml](./metadata.yaml) with information provided by the editor and type again:
+
+```bash
+$ make
+```
+
diff --git a/2022/41/appendix.tex b/2022/41/appendix.tex
diff --git a/2022/41/article.pdf b/2022/41/article.pdf
diff --git a/2022/41/article.py b/2022/41/article.py
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+../template/article.py
diff --git a/2022/41/article.tex b/2022/41/article.tex
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+% This is the entry point of the MLRC 2022 template
+\documentclass{rescience}
+% Place all your includes in the packages.tex file. Check the file for more information
+\input{packages}
+
+\begin{document}
+% DO NOT MODIFY THE CONTENTS OF header.tex
+\input{header.tex}
+
+% IMPORTANT NOTE
+% The title information should be added in metadata.yaml file. If you are compiling locally, then follow the instructions in https://github.com/ReScience/template. Else, you may edit the metadata.tex file directly. You can modify the fields "articleTITLE" and "articleABSTRACT".
+
+% IMPORTANT NOTE: Ensure that you DO NOT ADD any author information, as that would violate double blind leading to desk rejection.
+
+
+% Add the Reproducibility Summary in summary.tex
+\input{summary}
+\newpage
+% Add your paper contents in content.tex
+\input{content.tex}
+
+\hypersetup{linkcolor=black,urlcolor=darkgray}
+\renewcommand\emph[1]{{\bfseries #1}}
+\setlength\bibitemsep{0pt}
+\printbibliography
+
+% If you have appendix to add, add it in the appendix.tex file
+\input{appendix}
+
+\end{document}
diff --git a/2022/41/bibliography.bib b/2022/41/bibliography.bib
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+
+@InProceedings{Yuan21,
+  title = 	 {On Explainability of Graph Neural Networks via Subgraph Explorations},
+  author =       {Yuan, Hao and Yu, Haiyang and Wang, Jie and Li, Kang and Ji, Shuiwang},
+  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
+  pages = 	 {12241--12252},
+  year = 	 {2021},
+  volume = 	 {139},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {07},
+  publisher =    {PMLR},
+  pdf = 	 {http://proceedings.mlr.press/v139/yuan21c/yuan21c.pdf},
+  url = 	 {https://proceedings.mlr.press/v139/yuan21c.html},
+}
+
+
+
+@article{Luck20,
+    author  = {Katja Luck and Dae-Kyum Kim},
+    title   = {A reference map of the human binary protein interactome},
+    journal = {Nature},
+    year    = {2020},
+    volume  = {570},
+    pages   = {402--408},
+    month   = {4},
+    note    = {https://doi.org/10.1038/s41586-020-2188-x}
+}
+
+@book{Molnar19, 
+    title   = {Interpretable Machine Learning},
+    author  = {Christoph Molnar},
+    year    = {2019},
+    subtitle    = {A Guide for Making Black Box Models Explainable},
+    publisher   = {Bookdown}
+}
+
+@inproceedings{Ying19,
+     author     = {Ying, Zhitao and Bourgeois, Dylan and You, Jiaxuan and Zitnik, Marinka and Leskovec, Jure},
+     booktitle  = {Advances in Neural Information Processing Systems},
+     publisher  = {Curran Associates, Inc.},
+     title      = {{GNNE}xplainer: Generating Explanations for Graph Neural Networks},
+     volume     = {32},
+     year       = {2019}
+}
+
+@article{Zachary77,
+    title   = {An Information Flow Model for Conflict and Fission in Small Groups},
+    volume  = {33},
+    ISSN    = {2153-3806},
+    url     = {http://dx.doi.org/10.1086/jar.33.4.3629752},
+    DOI     = {10.1086/jar.33.4.3629752},
+    number  = {4},
+    journal = {Journal of Anthropological Research},
+    publisher   = {University of Chicago Press},
+    author  = {Zachary, Wayne W.},
+    year    = {1977},
+    month   = {12},
+    pages   = {452–473}
+}
+
+@misc{grover16,
+    title   = {Node2Vec: Scalable Feature Learning for Networks}, 
+    author  = {Aditya Grover and Jure Leskovec},
+    year    = {2016},
+    eprint  = {1607.00653},
+}
+
+@misc{kipf17,
+    title   = {Semi-Supervised Classification with Graph Convolutional Networks},
+    author  = {Thomas N. Kipf and Max Welling},
+    year    = {2017},
+    eprint  = {1609.02907},
+}
+
+@misc{wu19,
+    title   = {Simplifying Graph Convolutional Networks}, 
+    author  = {Felix Wu and Tianyi Zhang and Amauri Holanda de Souza Jr. au2 and Christopher Fifty and Tao Yu and Kilian Q. Weinberger},
+    year    = {2019},
+    eprint  = {1902.07153},
+    archivePrefix   = {arXiv},
+    primaryClass    = {cs.LG}
+}
+
+@misc{hamilton18,
+    title   = {Inductive Representation Learning on Large Graphs}, 
+    author  = {William L. Hamilton and Rex Ying and Jure Leskovec},
+    year    = {2018},
+    eprint  = {1706.02216},
+    archivePrefix   = {arXiv},
+    primaryClass    = {cs.SI}
+}
+
+@article{silver16,
+    author  = {{Silver}, David and {Huang}, Aja and {Maddison}, Chris J. and {Guez}, Arthur and {Sifre}, Laurent and {van den Driessche}, George and {Schrittwieser}, Julian and {Antonoglou}, Ioannis and {Panneershelvam}, Veda and {Lanctot}, Marc and {Dieleman}, Sander and {Grewe}, Dominik and {Nham}, John and {Kalchbrenner}, Nal and {Sutskever}, Ilya and {Lillicrap}, Timothy and {Leach}, Madeleine and {Kavukcuoglu}, Koray and {Graepel}, Thore and {Hassabis}, Demis},
+    title   = {Mastering the game of Go with deep neural networks and tree search},
+    journal = {Nature},
+    year    = {2016},
+    volume  = {529},
+    number  = {7587},
+    pages   = {484-489},
+    doi     = {10.1038/nature16961},
+    adsurl  = {https://ui.adsabs.harvard.edu/abs/2016Natur.529..484S},
+    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@misc{xu19,
+    title   = {How Powerful are Graph Neural Networks?}, 
+    author  = {Keyulu Xu and Weihua Hu and Jure Leskovec and Stefanie Jegelka},
+    year    = {2019},
+    eprint  = {1810.00826},
+    archivePrefix   = {arXiv},
+    primaryClass    = {cs.LG}
+}
+
+@misc{velickovic18,
+    title   = {Graph Attention Networks}, 
+    author  = {Petar Veličković and Guillem Cucurull and Arantxa Casanova and Adriana Romero and Pietro Liò and Yoshua Bengio},
+    year    = {2018},
+    eprint  = {1710.10903},
+    archivePrefix   = {arXiv},
+    primaryClass    = {stat.ML}
+}
+
+@article{sturmfels20,
+    author  = {Sturmfels, Pascal and Lundberg, Scott and Lee, Su-In},
+    title   = {Visualizing the Impact of Feature Attribution Baselines},
+    journal = {Distill},
+    year    = {2020},
+    note    = {https://distill.pub/2020/attribution-baselines},
+    doi     = {10.23915/distill.00022}
+}
+
+@article{DIG,
+    author  = {Meng Liu and Youzhi Luo and Limei Wang and Yaochen Xie and Hao Yuan and Shurui Gui and Haiyang Yu and Zhao Xu and Jingtun Zhang and Yi Liu and Keqiang Yan and Haoran Liu and Cong Fu and Bora M Oztekin and Xuan Zhang and Shuiwang Ji},
+    title   = {{DIG}: A Turnkey Library for Diving into Graph Deep Learning Research},
+    journal = {Journal of Machine Learning Research},
+    year    = {2021},
+    volume  = {22},
+    number  = {240},
+    pages   = {1--9},
+    url     = {http://jmlr.org/papers/v22/21-0343.html}
+}
+
+@misc{Yuan2021b,
+    title   = {Explainability in Graph Neural Networks: A Taxonomic Survey}, 
+    author  = {Hao Yuan and Haiyang Yu and Shurui Gui and Shuiwang Ji},
+    year    = {2021},
+    eprint  = {2012.15445},
+    archivePrefix   = {arXiv},
+    primaryClass    = {cs.LG}
+}
+
+@article {Debnath1991,
+	Title = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity},
+	Author = {Debnath, AK and Lopez de Compadre, RL and Debnath, G and Shusterman, AJ and Hansch, C},
+	DOI = {10.1021/jm00106a046},
+	Number = {2},
+	Volume = {34},
+	Month = {02},
+	Year = {1991},
+	Journal = {Journal of medicinal chemistry},
+	ISSN = {0022-2623},
+	Pages = {786—-797},
+	URL = {https://doi.org/10.1021/jm00106a046},
+}
+
+@InProceedings{remi07,
+    author = {Coulom, R{\'e}mi},
+    title = {Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search},
+    booktitle= {Computers and Games},
+    year={2007},
+    publisher={Springer Berlin Heidelberg},
+    address={Berlin, Heidelberg},
+    pages={72--83},
+    isbn={978-3-540-75538-8},
+}
+
+@book{shapley53,
+    author  = {Lloyd S. Shapley},
+    title   = {Contributions to the Theory of Games},
+    year    = {1953},
+    publisher   = {Princeton University Press},
+    pages   = {307--317},
+    volume = {2},
+}
+
+@misc{fey19,
+  doi = {10.48550/ARXIV.1903.02428},
+  url = {https://arxiv.org/abs/1903.02428},
+  author = {Fey, Matthias and Lenssen, Jan Eric},
+  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Fast Graph Representation Learning with PyTorch Geometric},
+  publisher = {arXiv},
+  year = {2019},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+@inproceedings{luo20,
+ author = {Luo, Dongsheng and Cheng, Wei and Xu, Dongkuan and Yu, Wenchao and Zong, Bo and Chen, Haifeng and Zhang, Xiang},
+ booktitle = {Advances in Neural Information Processing Systems},
+ pages = {19620--19631},
+ publisher = {Curran Associates, Inc.},
+ title = {Parameterized Explainer for Graph Neural Network},
+ url = {https://proceedings.neurips.cc/paper/2020/file/e37b08dd3015330dcbb5d6663667b8b8-Paper.pdf},
+ volume = {33},
+ year = {2020}
+}
+
+
+