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Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Algebraic Graph Theory
C. Godsil and G. Royle · 2001
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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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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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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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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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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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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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Novel positional encodings to enable tree-based transformers
Vighnesh Shiv and Chris Quirk · 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.
The logical expressiveness of graph neural networks
Pablo Barceló, Egor V Kostylev, Mikael Monet, Jorge Pérez, Juan Reutter, and Juan-Pablo Silva · 2020
Cited alongside, same era.
Coloring graph neural networks for node disambiguation
George Dasoulas, Ludovic Dos Santos, Kevin Scaman, and Aladin Virmaux · 2020
Cited alongside, same era.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
Cited alongside, same era.
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.
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
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Pure transformers are powerful graph learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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On the connection between mpnn and graph transformer
Chen Cai, Truong Son Hy, Rose Yu, and Yusu Wang · 2023
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WL meet VC
Christopher Morris, Floris Geerts, Jan Tönshoff, and Martin Grohe · 2023
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Christopher Morris, Gaurav Rattan, and Petra Mutzel · 2020
Cited alongside, same era.
Are transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, and Sanjiv Kumar · 2020
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.
Graph neural networks with local graph parameters
Pablo Barceló, Floris Geerts, Juan Reutter, and Maksimilian Ryschkov · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2021
Cited alongside, same era.
Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
Cited alongside, same era.
Luis Müller, Mikhail Galkin, Christopher Morris, and Ladislav Rampášek · 2023
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Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures
Nhat Khang Ngo, Truong Son Hy, and Risi Kondor · 2023
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Some might say all you need is sum
Eran Rosenbluth, Jan Toenshoff, and Martin Grohe · 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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Where did the gap go? reassessing the long-range graph benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, and Martin Grohe · 2023
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Lgi-gt: Graph transformers with local and global operators interleaving
Shuo Yin and Guoqiang Zhong · 2023
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Are targeted messages more effective?, 2024
Martin Grohe and Eran Rosenbluth · 2024
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