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Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis and data mining.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and Andrei A. Lehman · 1968
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An optimal lower bound on the number of variables for graph identifications
Jin-yi Cai, Martin Fürer, and Neil Immerman · 1992
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Shortest-path kernels on graphs
Karsten M. Borgwardt and Hans-Peter Kriegel · 2005
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten M. Borgwardt · 2009
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The weisfeiler-lehman method and graph isomorphism testing
B. Douglas · 2011
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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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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt · 2011
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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
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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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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
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Variational graph auto-encoders
Thomas N. Kipf and Max Welling · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Protein interface prediction using graph convolutional networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 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
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein · 2017
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Molgan: An implicit generative model for small molecular graphs
Retgk: Graph kernels based on return probabilities of random walks
Zhen Zhang, Mianzhi Wang, Yijian Xiang, Yan Huang, and Arye Nehorai · 2018
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A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S. Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabás Póczos, Ruosong Wang, and Keyulu Xu · 2019
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Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-lstm network
Md. Rezaul Karim, Michael Cochez, Joao Bosco Jares, Mamtaz Uddin, Oya Deniz Beyan, and Stefan Decker · 2019
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Understanding attention and generalization in graph neural networks
Boris Knyazev, Graham W Taylor, and Mohamed Amer · 2019
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Nicola De Cao and Thomas Kipf · 2018
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Embedding logical queries on knowledge graphs
William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Anonymous walk embeddings
Sergey Ivanov and Evgeny Burnaev · 2018
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Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C. Grossman · 2018
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Disentangled graph convolutional networks
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Knowledge graph convolutional networks for recommender systems
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Capsule graph neural network
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How powerful are graph neural networks?
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Random walk graph neural networks
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Agcn: Attention-based graph convolutional networks for drug-drug interaction extraction
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ASAP: adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha P. Talukdar · 2020
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