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Variants of Graph Neural Networks (GNNs) for representation learning have been proposed recently and achieved fruitful results in various fields.
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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The lifting scheme: A construction of second generation wavelets
Wim Sweldens. 1998 · 1998
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Collective Classification in Network Data
Prithviraj Sen, Galileo Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. 2008 · 2008
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Understanding the difficulty of training deep feedforward neural networks. In
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Wavelets on graphs via spectral graph theory
David K. Hammond, Pierre Vandergheynst, and Rémi Gribonval. 2011 · 2011
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013 · 2013
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Spectral networks and locally connected networks on graphs. In
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Deepwalk: Online learning of social representations. In
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014 · 2014
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Graph wavelets for multiscale community mining
Nicolas Tremblay and Pierre Borgnat. 2014 · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
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Image-based recommendations on styles and substitutes. In
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
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Gated Graph Sequence Neural Networks.. In
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016 · 2016
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Revisiting semi-supervised learning with graph embeddings. In
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016 · 2016
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Inductive Representation Learning on Large Graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Learning Structural Node Embeddings via Diffusion Wavelets. In
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec. 2018 · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks. In
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Takanori Maehara. 2019 · 2019
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Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey, William Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019 · 2019
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Simplifying Graph Convolutional Networks. In
Felix Wu, Amauri H. Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019 · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu. 2019 · 2019
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Graph Wavelet Neural Network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng. 2019 · 2019
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Cited alongside, same era.
Hypergraph Convolution and Hypergraph Attention
Song Bai, Feihu Zhang, and Philip H. S. Torr. 2019 · 2019
Cited alongside, same era.
Graph Neural Networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi. 2019 · 2019
Cited alongside, same era.
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2019 · 2019
Cited alongside, same era.
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, and Jiashi Feng. 2019 · 2019
Cited alongside, same era.
Graph representation learning via hard and channel-wise attention networks. In
Hongyang Gao and Shuiwang Ji. 2019 · 2019
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Power up! robust graph convolutional network against evasion attacks based on graph powering
Ming Jin, Heng Chang, Wenwu Zhu, and Somayeh Sojoudi. 2019 · 2019
Cited alongside, same era.
Graph Convolutional Networks for Temporal Action Localization. In
Runhao Zeng, Wenbing Huang, Mingkui Tan, Yu Rong, Peilin Zhao, Junzhou Huang, and Chuang Gan. 2019 · 2019
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Adaptive structural fingerprints for graph attention networks. In
Kai Zhang, Yaokang Zhu, Jun Wang, and Jie Zhang. 2019 · 2019
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A restricted black-box adversarial framework towards attacking graph embedding models. In
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang. 2020 · 2020
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GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding. In
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng. 2020 · 2020
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Implicit graph neural networks
Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, and Laurent El Ghaoui. 2020 · 2020
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Direct multi-hop attention based graph neural network
Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec. 2020 · 2020
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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020 · 2020
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AutoGL: A Library for Automated Graph Learning
Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, and Wenwu Zhu. 2021 · 2021
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Heterogeneous graph attention network. In
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019 · 2032
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