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Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community.
Alchemy: A quantum chemistry dataset for benchmarking ai models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, et al · 1906
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Operations with structures
László Lovász · 1967
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The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Lehman · 1968
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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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Parallel recognition of series-parallel graphs
David Eppstein · 1992
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A note on graded modal logic
Maarten De Rijke · 2000
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Weisfeiler-lehman refinement requires at least a linear number of iterations
Martin Fürer · 2001
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The description logic handbook: Theory, implementation and applications
Franz Baader · 2003
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Large networks and graph limits , volume 60
László Lovász · 2012
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Practical graph isomorphism, ii
Brendan D McKay and Adolfo Piperno · 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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Graph isomorphism in quasipolynomial time
László Babai · 2016
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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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Xavier Bresson and Thomas Laurent · 2017
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Homomorphisms are a good basis for counting small subgraphs
Radu Curticapean, Holger Dell, and Dániel Marx · 2017
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Graph Theory
Reinhard Diestel · 2017
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On the combinatorial power of the weisfeiler-lehman algorithm
Martin Fürer · 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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Descriptive complexity, canonisation, and definable graph structure theory , volume 47
Martin Grohe · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex 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 Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Lovász meets weisfeiler and leman
Holger Dell, Martin Grohe, and Gaurav Rattan · 2018
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An exponential lower bound for individualization-refinement algorithms for graph isomorphism
Daniel Neuen and Pascal Schweitzer · 2018
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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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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 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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Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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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, et al · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Distance-restricted folklore weisfeiler-leman gnns with provable cycle counting power
Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein · 2022
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Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt · 2022
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Cycle to clique (cy2c) graph neural network: A sight to see beyond neighborhood aggregation
Yun Young Choi, Sun Woo Park, Youngho Woo, and U Jin Choi · 2022
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
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How powerful are k-hop message passing graph neural networks
Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, and Muhan Zhang · 2022
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Junru Zhou, Jiarui Feng, Xiyuan Wang, and Muhan Zhang · 2019
Cited alongside, same era.
On weisfeiler-leman invariance: Subgraph counts and related graph properties
Vikraman Arvind, Frank Fuhlbrück, Johannes Köbler, and Oleg Verbitsky · 2020
Cited alongside, same era.
The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan-Pablo Silva · 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
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
Cited alongside, same era.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Cited alongside, same era.
Fabrizio Frasca, Beatrice Bevilacqua, Michael M Bronstein, and Haggai Maron · 2022
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Expressiveness and approximation properties of graph neural networks
Floris Geerts and Juan L Reutter · 2022
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Topological graph neural networks
Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, and Karsten Borgwardt · 2022
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Your transformer may not be as powerful as you expect
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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Speqnets: Sparsity-aware permutation-equivariant graph networks
Christopher Morris, Gaurav Rattan, Sandra Kiefer, and Siamak Ravanbakhsh · 2022
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A theoretical comparison of graph neural network extensions
Pál András Papp and Roger Wattenhofer · 2022
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Ordered subgraph aggregation networks
Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, and Christopher Morris · 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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Message passing all the way up
Petar Veličković · 2022
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Plane: Representation learning over planar graphs
Radoslav Dimitrov, Zeyang Zhao, Ralph Abboud, and İsmail İlkan Ceylan · 2023
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Jiarui Feng, Lecheng Kong, Hao Liu, Dacheng Tao, Fuhai Li, Muhan Zhang, and Yixin Chen · 2023
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Cin++: Enhancing topological message passing
Lorenzo Giusti, Teodora Reu, Francesco Ceccarelli, Cristian Bodnar, and Pietro Liò · 2023
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Boosting the cycle counting power of graph neural networks with i$^2$-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma, and Muhan Zhang · 2023
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Sign and basis invariant networks for spectral graph representation learning
Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2023
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Path neural networks: Expressive and accurate graph neural networks
Gaspard Michel, Giannis Nikolentzos, Johannes F Lutzeyer, and Michalis Vazirgiannis · 2023
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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 · 2023
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Homomorphism-distinguishing closedness for graphs of bounded tree-width
Daniel Neuen · 2023
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Equivariant polynomials for graph neural networks
Omri Puny, Derek Lim, Bobak Kiani, Haggai Maron, and Yaron Lipman · 2023
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Weisfeiler-leman and graph spectra
Gaurav Rattan and Tim Seppelt · 2023
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Logical equivalences, homomorphism indistinguishability, and forbidden minors
Tim Seppelt · 2023
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The power of recursion in graph neural networks for counting substructures
Behrooz Tahmasebi, Derek Lim, and Stefanie Jegelka · 2023
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n n -WL: A new hierarchy of expressivity for graph neural networks
Qing Wang, Dillon Ze Chen, Asiri Wijesinghe, Shouheng Li, and Muhammad Farhan · 2023
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