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Graph isomorphism testing is usually approached via the comparison of graph invariants.
Describing graphs: A first-order approach to graph canonization
Neil Immerman and Eric Lander · 1990
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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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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Spectral graph theory
Daniel Spielman · 2009
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Fixed-point definability and polynomial time on graphs with excluded minors
Martin Grohe · 2012
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Deep graph kernels
Pinar Yanardag and S. V. N. Vishwanathan · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Earlier work this paper cites.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Deep functional maps: Structured prediction for dense shape correspondence
Or Litany, Tal Remez, Emanuele Rodol‘a, Alex Bronstein, and Michael Bronstein · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Fast solvers for solving shape matching by time integration
Martin Bähr, Robert Dachsel, and Michael Breuß · 2018
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Graph neural networks for icecube signal classification
Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat Prabhat, Wahid Bhimji, Michael Bronstein, Spencer Klein, and Joan Bruna · 2018
Cited alongside, same era.
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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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2019
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A survey on the expressive power of graph neural networks
Ryoma Sato · 2020
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A deep learning approach to antibiotic discovery
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackermann, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, and James J. Collins · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
The weisfeiler–leman dimension of planar graphs is at most 3
Sandra Kiefer, Ilia Ponomarenko, and Pascal Schweitzer · 2019
Cited alongside, same era.
Fake news detection on social media using geometric deep learning
Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein · 2019
Cited alongside, same era.
Weisfeiler and lehman go cellular: Cw networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido F Montufar, and Michael Bronstein
Cited in the paper.
Weisfeiler and lehman go topological: Message passing simplicial networks
Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Lio, and Michael Bronstein
Cited in the paper.
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Breaking the limits of message passing graph neural networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Pascal Vasseur, Sébastien Adam, and Paul Honeine · 2021
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Directional graph networks
Dominique Beani, Saro Passaro, Vincent Létourneau, Will Hamilton, Gabriele Corso, and Pietro Liò · 2021
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Weisfeiler–leman, graph spectra, and random walks
Gaurav Rattan and Tim Seppelt · 2021
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