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Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs.
A congruence theorem for trees
Paul J. Kelly · 1957
Earlier work this paper cites.
A collection of mathematical problems , volume 8
Stanislaw M. Ulam · 1960
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The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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Semi-supervised classification with graph convolutional networks
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Automatic chemical design using a data-driven continuous representation of molecules
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Earlier work this paper cites.
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Covariant compositional networks for learning graphs
Truong Son Hy, Shubhendu Trivedi, Horace Pan, Brandon M Anderson, and Risi Kondor · 2019
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Universal invariant and equivariant graph neural networks
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Earlier work this paper cites.
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
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Earlier work this paper cites.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Small relu networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, and Ali Jadbabaie · 2019
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The surprising power of graph neural networks with random node initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 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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Principal neighbourhood aggregation for graph nets
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Natural graph networks
Pim de Haan, Taco S Cohen, and Max Welling · 2020
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Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B R Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L Blundell, Michael M Bronstein, and Jake P Taylor-King · 2021
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Rethinking graph transformers with spectral attention
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Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
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Weisfeiler and leman go machine learning: The story so far
Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M Kriege, Martin Grohe, Matthias Fey, and Karsten Borgwardt · 2021
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Implicit mle: Backpropagating through discrete exponential family distributions
Mathias Niepert, Pasquale Minervini, and Luca Franceschi · 2021
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Benchmarking graph neural networks
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Hierarchical inter-message passing for learning on molecular graphs
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The expressive power of kth-order invariant graph networks
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Open graph benchmark: Datasets for machine learning on graphs
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On learning sets of symmetric elements
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Global attention improves graph networks generalization
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Dropgnn: Random dropouts increase the expressiveness of graph neural networks
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Autobahn: Automorphism-based graph neural nets
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Nested graph neural networks
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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 · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
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Graph neural networks with learnable structural and positional representations
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A theoretical comparison of graph neural network extensions
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Ordered subgraph aggregation networks
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From stars to subgraphs: Uplifting any GNN with local structure awareness
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