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Graph Neural Networks (GNNs) are the state-of-the-art model for machine learning on graph-structured data.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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Random walks, universal traversal sequences, and the complexity of maze problems
Romas Aleliunas, Richard M Karp, Richard J Lipton, László Lovász, and Charles Rackoff · 1979
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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1990
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Random walks on graphs
László Lovász · 1993
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The electrical resistance of a graph captures its commute and cover times
Ashok K Chandra, Prabhakar Raghavan, Walter L Ruzzo, Roman Smolensky, and Prasoon Tiwari · 1996
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Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Xavier Bresson and Thomas Laurent · 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
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan Pablo Silva · 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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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
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On the universality of invariant networks
Haggai Maron, Ethan Fetaya, Nimrod Segol, and Yaron Lipman · 2019
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Fake news detection on social media using geometric deep learning
Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein · 2019
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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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Approximation ratios of graph neural networks for combinatorial problems
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Unveiling the predictive power of static structure in glassy systems
Victor Bapst, Thomas Keck, A Grabska-Barwińska, Craig Donner, Ekin Dogus Cubuk, Samuel S Schoenholz, Annette Obika, Alexander WR Nelson, Trevor Back, Demis Hassabis, et al · 2020
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A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 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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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Redundancy-free message passing for graph neural networks
Rongqin Chen, Shenghui Zhang, Ye Li, et al · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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Expander graph propagation
Andreea Deac, Marc Lackenby, and Petar Veličković · 2022
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Graph neural networks as gradient flows
Francesco Di Giovanni, James Rowbottom, Benjamin P Chamberlain, Thomas Markovich, and Michael M Bronstein · 2022
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Expressiveness and approximation properties of graph neural networks
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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What graph neural networks cannot learn: depth vs width
Andreas Loukas · 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, et al · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Expressive power of invariant and equivariant graph neural networks
Waiss Azizian and Marc Lelarge · 2021
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Towards combinatorial invariance for kazhdan-lusztig polynomials
Charles Blundell, Lars Buesing, Alex Davies, Petar Veličković, and Geordie Williamson · 2021
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Message passing neural pde solvers
Johannes Brandstetter, Daniel E Worrall, and Max Welling · 2021
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Floris Geerts and Juan L Reutter · 2022
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Learnable commutative monoids for graph neural networks
Euan Ong and Petar Veličković · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampasek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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On the ability of graph neural networks to model interactions between vertices
Noam Razin, Tom Verbin, and Nadav Cohen · 2022
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Graph-coupled oscillator networks
T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, and Michael Bronstein · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2022
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Understanding oversquashing in gnns through the lens of effective resistance
Mitchell Black, Amir Nayyeri, Zhengchao Wan, and Yusu Wang · 2023
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Graph neural networks at the large hadron collider
Gage DeZoort, Peter W Battaglia, Catherine Biscarat, and Jean-Roch Vlimant · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael Bronstein · 2023
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Multi-scale message passing neural pde solvers
Léonard Equer, T Konstantin Rusch, and Siddhartha Mishra · 2023
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On the expressive power of geometric graph neural networks
Chaitanya K Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, and Pietro Lio · 2023
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FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Kedar Karhadkar, Pradeep Kr. Banerjee, and Guido Montufar · 2023
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Deep learning-guided discovery of an antibiotic targeting acinetobacter baumannii
Gary Liu, Denise B. Catacutan, Khushi Rathod, Kyle Swanson, Wengong Jin, Jody C. Mohammed, Anush Chiappino-Pepe, Saad A. Syed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Michael G. Surette, Brian K. Coombes, Tommi Jaakkola, Regina Barzilay, James J. Collins, and Jonathan M. Stokes · 2023
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Exphormer: Sparse transformers for graphs
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J Sutherland, and Ali Kemal Sinop · 2023
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Everything is connected: Graph neural networks
Petar Veličković · 2023
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Is deep learning a useful tool for the pure mathematician?
Geordie Williamson · 2023
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Rethinking the expressive power of gnns via graph biconnectivity
Bohang Zhang, Shengjie Luo, Liwei Wang, and Di He · 2023
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