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Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Combining neural networks with personalized pagerank for classification on graphs
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, and Keyulu Xu · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S Zemel · 2019
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Sergey Ivanov and Evgeny Burnaev · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Simplifying graph convolutional networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 2019
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Capsule graph neural network
Zhang Xinyi and Lihui Chen · 2019
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen
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Retgk: Graph kernels based on return probabilities of random walks
Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, and Arye Nehorai
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Hongyang Gao and Shuiwang Ji · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Hoang NT and Takanori Maehara · 2019
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Understanding the representation power of graph neural networks in learning graph topology
Nima Dehmamy, Albert-László Barabási, and Rose Yu · 2019
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