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Graph Neural Networks (GNNs) have received a lot of interest in the recent times.
A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019 · 1901
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Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2019 · 1904
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Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 1905
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Discriminative structural graph classification
Younjoo Seo, Andreas Loukas, and Nathanaël Perraudin. 2019 · 1905
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Improving Graph Attention Networks with Large Margin-based Constraints
Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec. 2019 · 1910
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 1912
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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A greedoid polynomial which distinguishes rooted arborescences
Gary Gordon and Elizabeth McMahon. 1989 · 1989
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A new model for learning in graph domains. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , Vol. 2. 729–734 vol. 2
M. Gori, G. Monfardini, and F. Scarselli. 2005 · 2005
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Graph Kernels between Point Clouds. In Proceedings of the 25th International Conference on Machine Learning (Helsinki, Finland) (ICML ’08) . Association for Computing Machinery, New York, NY, USA, 25–32
Francis R. Bach. 2008 · 2008
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The Graph Neural Network Model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. 2009 · 2008
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Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Cited alongside, same era.
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. 2011 · 2011
Cited alongside, same era.
How to grow a mind: Statistics, structure, and abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman. 2011 · 2011
Cited alongside, same era.
Graphs theory and applications: with exercises and problems
Jean-Claude Fournier. 2013 · 2013
Cited alongside, same era.
Macro-and micro-averaged evaluation measures [[basic draft]]
Vincent Van Asch. 2013 · 2013
Cited alongside, same era.
Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein. 2017 · 2017
Later among the works it cites.
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. 2018 · 2018
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Stochastic Training of Graph Convolutional Networks with Variance Reduction. In International Conference on Machine Learning . 941–949
Jianfei Chen, Jun Zhu, and Le Song. 2018 · 2018
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Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, and Xiao ming Wu. 2018 · 2018
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Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014 · 2014
Cited alongside, same era.
Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations (San Diego, California) (ICLR ’15)
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS . 3844–3852
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks. In Proceedings of the 3rd International Conference on Learning Representations
Brenden M. Lake and Marco Baroni. 2017 · 2017
Cited alongside, same era.
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Later among the works it cites.
Graph Attention Networks. In International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Later among the works it cites.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Brussels, Belgium, 349–357
Zhichun Wang, Qingsong Lv, Xiaohan Lan, and Yu Zhang. 2018 · 2018
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Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, Florence, Italy, 4309–4316
Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, and Sophia Ananiadou. 2019 · 2019
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How Powerful are Graph Neural Networks?. In International Conference on Learning Representations
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
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Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020 · 2020
Closest in time.
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020 · 2020
Closest in time.