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Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) algorithm.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and Andrei A Lehman · 1968
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Canonical labelling of graphs in linear average time
László Babai and Ludik Kucera · 1979
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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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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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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Pebble games and linear equations
Martin Grohe and Martin Otto · 2015
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Community detection and stochastic block models: recent developments
Emmanuel Abbe · 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
Will Hamilton, Zhitao 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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
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Motifnet: a motif-based graph convolutional network for directed graphs
Federico Monti, Karl Otness, and Michael M Bronstein · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 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
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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DropGNN: random dropouts increase the expressiveness of graph neural networks
Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer · 2021
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Ego-gnns: Exploiting ego structures in graph neural networks
Dylan Sandfelder, Priyesh Vijayan, and William L Hamilton · 2021
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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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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
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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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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Cited alongside, same era.
Distance encoding: Design provably more powerful neural networks for graph representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2020
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Identity-aware graph neural networks
Jiaxuan You, Jonathan M Gomes-Selman, Rex Ying, and Jure Leskovec · 2021
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Decoupling the depth and scope of graph neural networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen · 2021
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Nested graph neural networks
Muhan Zhang and Pan Li · 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 · 2022
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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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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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Understanding and extending subgraph gnns by rethinking their symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael M Bronstein, and Haggai Maron · 2022
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Global self-attention as a replacement for graph convolution
Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian · 2022
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Theory of graph neural networks: Representation and learning
Stefanie Jegelka · 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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Ordered subgraph aggregation networks
Chendi Qian, Gaurav Rattan, Floris Geerts, Mathias Niepert, and Christopher Morris · 2022
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A new perspective on" how graph neural networks go beyond weisfeiler-lehman?"
Asiri Wijesinghe and Qing Wang · 2022
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From stars to subgraphs: Uplifting any GNN with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2022
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Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K Dokania, Mark Coates, Philip Torr, and Ser-Nam Lim · 2023
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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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