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We introduce Automorphism-based graph neural networks (Autobahn), a new family of graph neural networks.
A congruence theorem for trees
Paul J Kelly · 1957
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A collection of mathematical problems
Stanislaw M Ulam · 1960
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An optimal lower bound on the number of variables for graph identification
Jin-Yi Cai, Martin Fürer, and Neil Immerman · 1992
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An improved algorithm for matching large graphs
Luigi Pietro Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento · 2001
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A (sub)graph isomorphism algorithm for matching large graphs
Luigi P Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento · 2004
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PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results
Steven L Dixon, Alexander M Smondyrev, Eric H Knoll, Shashidhar N Rao, David E Shaw, and Richard A Friesner · 2006
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The graph neural network model
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Turboiso: Towards ultrafast and robust subgraph isomorphism search in large graph databases
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Deepwalk: Online learning of social representations
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Deep convolutional networks on graph-structured data
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gomez-Bombarelli, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P. Adams · 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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Group equivariant convolutional networks
Taco S. Cohen and Max Welling · 2016
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Geometric deep learning: going beyond Euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 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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Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets
Alexandru Korotcov, Valery Tkachenko, Daniel P Russo, and Sean Ekins · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Steerable CNNs
Taco S. Cohen and Max Welling · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Póczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Risi Kondor and Shubhendu Trivedi · 2018
On Weisfeiler-Leman invariance: Subgraph counts and related graph properties
Vikraman Arvind, Frank Fuhlbrück, Johannes Köbler, and Oleg Verbitsky · 2020
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Subgraph neural networks
Emily Alsentzer, Samuel Finlayson, Michelle Li, and Marinka Zitnik · 2020
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein · 2020
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Hierarchical inter-message passing for learning on molecular graphs
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Invariant and equivariant graph networks
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Predicting molecular properties with covariant compositional networks
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How powerful are graph neural networks?
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A review of the application of machine learning and data mining approaches in continuum materials mechanics
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Advances of machine learning in molecular modeling and simulation
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Erik H. Thiede, Truong Son Hy, and Risi Kondor · 2020
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Natural graph networks
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Building powerful and equivariant graph neural networks with structural message-passing
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The expressive power of kth-order invariant graph networks
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Graph convolutions that can finally model local structure
Rémy Brossard, Oriel Frigo, and David Dehaene · 2020
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Data-driven strategies for accelerated materials design
Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, Riley J Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao, et al · 2021
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Directional graph networks
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Graph learning with 1D convolutions on random walks
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Neural message passing on high order paths
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