「グラフニューラルネットワーク」(オーム社)に関するサポート情報を掲載します。
オーム社 https://www.ohmsha.co.jp/book/9784274228872/
アマゾン https://www.amazon.co.jp/dp/4274228878/
グラフニューラルネットワーク: PyTorchによる実装
村田 剛志 著
本体3,200円＋税
A5判／248頁
ISBN：978-4-274-22887-2
発売日：2022/07/20
発行元：オーム社
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13ページ
"Graph Neural Networks: A Review of Methods and Applications"
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun
AI Open, Vol. 1, pp.57-81, 2020.
https://doi.org/10.1016/j.aiopen.2021.01.001 -
14ページ
Traffic prediction with advanced Graph Neural Networks
DeepMind, September 3, 2020.
https://www.deepmind.com/blog/traffic-prediction-with-advanced-graph-neural-networks -
17ページ
Graph Methods for COVID-19 Response
William L. Hamilton
https://cs.mcgill.ca/~wlh/comp766/files/graphs-against-covid.pdf
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23ページ
A Survey on Network Embedding
Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu
IEEE Transactions on Knowledge and Data Engineering, Vol. 31, No. 5, pp. 833-852, 2019.
https://doi.org/10.1109/TKDE.2018.2849727 -
31ページ
DeepWalk: online learning of social representations
Bryan Perozzi, Rami Al-Rfou, Steven Skiena
The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'14), pp.701-710, 2014.
https://doi.org/10.1145/2623330.2623732
著者Perozziによるコード
https://github.com/phanein/deepwalk
paperswithcode.comにおけるサイト
https://paperswithcode.com/method/deepwalk -
36ページ
LINE: Large-scale Information Network Embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei
Proceedings of the 24th International Conference on World Wide Web (WWW'15) pp.1067-1077, 2015.
https://doi.org/10.1145/2736277.2741093
著者Tangによるコード
https://github.com/tangjianpku/LINE
paperswithcode.comにおけるサイト
https://paperswithcode.com/method/line -
39ページ
node2vec: Scalable Feature Learning for Networks
Aditya Grover, Jure Leskovec
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16) pp.855-864, 2016.
https://doi.org/10.1145/2939672.2939754
著者Groverらによるサイト
https://snap.stanford.edu/node2vec/
paperswithcode.comにおけるサイト
https://paperswithcode.com/method/node2vec -
40ページ
GraRep: Learning Graph Representations with Global Structural Information
Shaosheng Cao, Wei Lu, Qiongkai Xu
Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM'15) pp.891-900, 2015.
https://doi.org/10.1145/2806416.2806512
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/grarep-learning-graph-representations-with -
44ページ
グラフエンベディング手法の比較のためのPythonコード
https://github.com/yijiaozhang/hypercompare
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60ページ
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst
Advances in Neural Information Processing Systems 29 (NIPS 2016), 2016.
https://papers.nips.cc/paper/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering
https://arxiv.org/abs/1606.09375
著者Defferrardによるコード
https://github.com/mdeff/cnn_graph
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/convolutional-neural-networks-on-graphs-with -
62ページ
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf, Max Welling
5th International Conference on Learning Representations (ICLR 2017), 2017.
https://arxiv.org/abs/1609.02907
著者Kipfによるコード
https://github.com/tkipf/gcn
著者Kipfによるサイト
http://tkipf.github.io/graph-convolutional-networks/ -
65ページ
Learning Convolutional Neural Networks for Graphs
Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), 2016.
https://arxiv.org/abs/1605.05273
コード
https://github.com/Lookuz/PATCHY-SAN
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/learning-convolutional-neural-networks-for -
67ページ
Diffusion-Convolutional Neural Networks
James Atwood, Don Towsley
Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS 2016), 2016.
https://arxiv.org/abs/1511.02136
コード
https://github.com/jcatw/dcnn
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/diffusion-convolutional-neural-networks -
69ページ
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure Leskovec
Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), 2017.
https://arxiv.org/abs/1706.02216
コード
https://github.com/williamleif/GraphSAGE
著者Hamiltonらによるサイト
http://snap.stanford.edu/graphsage/
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/inductive-representation-learning-on-large
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74ページ
Structural Deep Network Embedding
Daixin Wang, Peng Cui, Wenwu Zhu
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pp.1225–1234, 2016
https://doi.org/10.1145/2939672.2939753
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/structural-deep-network-embedding -
80ページ
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
6th International Conference on Learning Representations (ICLR 2018), 2018.
https://arxiv.org/abs/1710.10903
コード
https://github.com/PetarV-/GAT
著者Velickovicらによるサイト
https://petar-v.com/GAT/
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/graph-attention-networks -
83ページ
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
Proceedings of the 36th International Conference on Machine Learning (ICML 2019), 2019.
https://arxiv.org/abs/1902.07153
コード
https://github.com/Tiiiger/SGC
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/simplifying-graph-convolutional-networks/ -
86ページ
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
7th International Conference on Learning Representations (ICLR 2019), 2019.
https://arxiv.org/abs/1810.00826
コード
https://github.com/weihua916/powerful-gnns -
87ページ
paperswithcode.comにおけるサイト
https://paperswithcode.com/paper/how-powerful-are-graph-neural-networks/
A Survey on The Expressive Power of Graph Neural Networks
Ryoma Sato
https://arxiv.org/abs/2003.04078 -
88ページ
Adversarial Attacks and Defenses on Graphs
Wei Jin, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang
ACM SIGKDD Explorations Newsletter, Vol.22, Issue 2, pp.19–34, 2020.
https://doi.org/10.1145/3447556.3447566 -
89ページ
DeepRobust
https://github.com/DSE-MSU/DeepRobust -
96ページ
Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji
https://arxiv.org/abs/2012.15445
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103ページ
Pythonの本家のサイト
https://www.python.org/
Python情報サイト
https://www.python.jp/ -
106ページ
NumPyのquickstartのサイト
https://numpy.org/doc/stable/user/quickstart.html -
109ページ
SciPyのドキュメンテーション
https://docs.scipy.org/doc/scipy/index.html -
112ページ
pandasのドキュメンテーション
https://pandas.pydata.org/docs/index.html -
113ページ
Matplotlibの例
https://matplotlib.org/stable/gallery/index.html -
115ページ
Matplotlibのサイト
https://matplotlib.org/stable/index.html -
116ページ
seabornの例
https://seaborn.pydata.org/examples/index.html -
117ページ
seaborn.jointplot
https://seaborn.pydata.org/generated/seaborn.jointplot.html -
118ページ
seabornのサイト
https://seaborn.pydata.org/ -
119ページ
scikit-learnのサイト
https://scikit-learn.org/stable/ -
120ページ
scikit-learn algorithm cheat-sheet
https://scikit-learn.org/stable/tutorial/machine_learning_map/ -
122ページ
Laurens van der Maatenによるサイト(t-SNE)
https://lvdmaaten.github.io/tsne/ -
123ページ
How to use t-SNE Effectively
https://distill.pub/2016/misread-tsne/ -
125ページ
Scipy Lecture Notes (英語)
https://scipy-lectures.org/
Scipy Lecture Notes (日本語訳)
http://www.turbare.net/transl/scipy-lecture-notes/ -
126ページ
Jupyter Notebook
https://jupyter.org/
Anaconda
https://www.anaconda.com/ -
127ページ
JupyterLab Desktop App
https://github.com/jupyterlab/jupyterlab-desktop -
128ページ
Colaboratory
https://colab.research.google.com/
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135ページ
PyTorch Get Started
https://pytorch.org/get-started/locally/
Colab Notebooks and Video Tutorials
https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html -
136ページ
PyTorchチュートリアル(日本語翻訳版)
https://yutaroogawa.github.io/pytorch_tutorials_jp/
PyTorch Tutorials
https://pytorch.org/tutorials/
PyTorch Documentation
https://pytorch.org/docs/stable/
PyTorch basics (PyTorch Geometric Tutorial)
https://antoniolonga.github.io/Pytorch_geometric_tutorials/posts/post2.html -
147ページ
Loss Functions (PyTorch)
https://pytorch.org/docs/stable/nn.html#loss-functions -
159ページ
PyTorch Tutorials
https://pytorch.org/tutorials/
PyTorchチュートリアル(日本語翻訳版)
https://yutaroogawa.github.io/pytorch_tutorials_jp/ -
160ページ
PyTorch Geometric (PyG)
https://github.com/pyg-team/pytorch_geometric
Deeo Graph Library (DGL)
https://www.dgl.ai/ -
161ページ
Graph Nets
https://github.com/deepmind/graph_nets -
162ページ
Lightning
https://www.pytorchlightning.ai/
catalyst
https://github.com/catalyst-team/catalyst
fastai
https://docs.fast.ai/
ignite
https://github.com/pytorch/ignite -
163ページ
Introduction by Example (PyG Documentation)
https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html -
167ページ
TUDataset
http://graphkernels.cs.tu-dortmund.de -
177ページ
Colab Notebooks and Video Tutorials
https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html
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214ページ
Introduction to Graph Neural Networks
Zhiyuan Liu, Jie Zhou
Morgan & Claypool Publishers, 2020.
https://doi.org/10.2200/S00980ED1V01Y202001AIM045
Graph Representation Learning
William L. Hamilton
Morgan & Claypool publishers, 2020.
https://doi.org/10.2200/S01045ED1V01Y202009AIM046
(preprint)
https://www.cs.mcgill.ca/~wlh/grl_book/ -
215ページ
Deep Learning on Graphs
Yao Ma, Jiliang Tang
Cambridge University Press, 2021.
https://doi.org/10.1017/9781108924184
(preprint)
https://web.njit.edu/~ym329/dlg_book/
Graph Neural Networks -- Foundations, Frontiers, and Applications
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao
Springer, 2022.
https://doi.org/10.1007/978-981-16-6054-2
(preprint)
https://graph-neural-networks.github.io/ -
216ページ
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, Issue 1, pp.4-24, 2021.
https://doi.org/10.1109/TNNLS.2020.2978386 -
217ページ
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun
AI Open, Vol. 1, pp.57-81, 2020.
https://doi.org/10.1016/j.aiopen.2021.01.001
Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
IEEE Transactions on Knowledge and Data Engineering, Vol. 34, pp. 249-270, 2022.
https://doi.org/10.1109/TKDE.2020.2981333 -
218ページ
CS224W: Machine Learning with Graphs
Stanford / Fall 2022
http://web.stanford.edu/class/cs224w/
Pytorch Geometric Tutorial
Antonio Longa, Gabriele Santin, Giovanni Pellegrini
https://antoniolonga.github.io/Pytorch_geometric_tutorials/
Colab Notebooks and Video Tutorials
https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html -
219ページ
Graph Neural Networks
https://hhaji.github.io/Deep-Learning/Graph-Neural-Networks/
Must-read papers on GNN
https://github.com/thunlp/GNNPapers
Papers with codes – Graphs
https://paperswithcode.com/area/graphs
Awesome resources on Graph Neural Networks
https://github.com/GRAND-Lab/Awesome-Graph-Neural-Networks -
220ページ
Open Graph Benchmark (OGB)
https://ogb.stanford.edu/