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Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 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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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 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
Martin Grohe · 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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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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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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris, Gaurav Rattan, and Petra Mutzel · 2020
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On Weisfeiler-Leman invariance: Subgraph counts and related graph properties
V. Arvind, Frank Fuhlbrück, Johannes Köbler, and Oleg Verbitsky · 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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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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The expressive power of kth-order invariant graph networks
Floris Geerts · 2020
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Cited alongside, same era.
Nested graph neural networks
Muhan Zhang and Pan Li · 2021
Cited alongside, same era.
Weisfeiler and lehman go cellular: CW networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montufar, and Michael M. Bronstein · 2021
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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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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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Geodesic graph neural network for efficient graph representation learning
Lecheng Kong, Yixin Chen, 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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Dylan Sandfelder, Priyesh Vijayan, and William L. Hamilton · 2021
Cited alongside, same era.
Identity-aware graph neural networks
Jiaxuan You, Jonathan M Gomes-Selman, Rex Ying, and Jure Leskovec · 2021
Cited alongside, same era.
DropGNN: Random dropouts increase the expressiveness of graph neural networks
Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer · 2021
Cited alongside, same era.
Reconstruction for powerful graph representations
Leonardo Cotta, Christopher Morris, and Bruno Ribeiro · 2021
Cited alongside, same era.
Labeling trick: A theory of using graph neural networks for multi-node representation learning
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin · 2021
Cited alongside, same era.
Expressive power of invariant and equivariant graph neural networks
Waiss Azizian and marc lelarge · 2021
Cited alongside, same era.
The surprising power of graph neural networks with random node initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2021
Cited alongside, same era.
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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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Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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WL meet VC
Christopher Morris, Floris Geerts, Jan Tönshoff, and Martin Grohe · 2023
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From relational pooling to subgraph GNNs: A universal framework for more expressive graph neural networks
Cai Zhou, Xiyuan Wang, and Muhan Zhang · 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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Efficiently counting substructures by subgraph gnns without running gnn on subgraphs, 2023
Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang, and Muhan Zhang · 2023
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Equivariant polynomials for graph neural networks, 2023
Omri Puny, Derek Lim, Bobak T. Kiani, Haggai Maron, and Yaron Lipman · 2023
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Towards better evaluation of gnn expressiveness with brec dataset, 2023
Yanbo Wang and Muhan Zhang · 2023
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Specformer: Spectral graph neural networks meet transformers
Deyu Bo, Chuan Shi, Lele Wang, and Renjie Liao · 2023
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