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Graph transformers (GTs) have emerged as a promising architecture that is theoretically more expressive than message-passing graph neural networks (GNNs).
The generalized weierstrass approximation theorem
Marshall H Stone · 1948
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Polynomial theory of complex systems
Alexey Grigorevich Ivakhnenko · 1971
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The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation
Yoan Shin and Joydeep Ghosh · 1991
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A sigma-pi-sigma neural network (spsnn)
Chien-Kuo Li · 2003
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Ridge polynomial networks in pattern recognition
Christodoulos Voutriaridis, Yiannis S Boutalis, and Basil G Mertzios · 2003
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 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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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 Eric Lenssen · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al · 2019
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Are transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Kumar · 2019
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Expressive power of invariant and equivariant graph neural networks
Waiss Azizian and Marc Lelarge · 2020
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Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann · 2020
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
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Adaptive universal generalized pagerank graph neural network
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2020
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P-nets: Deep polynomial neural networks
Grigorios G Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, and Stefanos Zafeiriou · 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
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Finding global homophily in graph neural networks when meeting heterophily
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian · 2022
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Simplifying approach to node classification in graph neural networks
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata · 2022
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cosformer: Rethinking softmax in attention
Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, and Yiran Zhong · 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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Gradient gating for deep multi-rate learning on graphs
T Konstantin Rusch, Benjamin P Chamberlain, Michael W Mahoney, Michael M Bronstein, and Siddhartha Mishra · 2022
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Péter Mernyei and Cătălina Cangea · 2020
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Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
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A survey on the expressive power of graph neural networks
Ryoma Sato · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Accurate learning of graph representations with graph multiset pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang · 2021
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From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers
Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, and Thomas Weingarten · 2021
Cited alongside, same era.
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Nodeformer: A scalable graph structure learning transformer for node classification
Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan · 2022
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra · 2022
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Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
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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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How does over-squashing affect the power of gnns?
Francesco Di Giovanni, T Konstantin Rusch, Michael M Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, and Petar Veličković · 2023
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GOAT: A global transformer on large-scale graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, and Tom Goldstein · 2023
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Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet Kumar Dokania, Mark Coates, Philip H. S. Torr, and Ser Nam Lim · 2023
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A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova · 2023
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Equivariant polynomials for graph neural networks
Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, and Yaron Lipman · 2023
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Edge directionality improves learning on heterophilic graphs
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, and Michael Bronstein · 2023
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Exphormer: Sparse transformers for graphs
Hamed Shirzad, Ameya Velingker, B. Venkatachalam, Danica J. Sutherland, and Ali Kemal Sinop · 2023
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Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing
Yunchong Song, Chenghu Zhou, Xinbing Wang, and Zhouhan Lin · 2023
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DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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Are more layers beneficial to graph transformers?
Haiteng Zhao, Shuming Ma, Dongdong Zhang, Zhi-Hong Deng, and Furu Wei · 2023
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Tpugraphs: A performance prediction dataset on large tensor computational graphs
Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Michael Burrows, Charith Mendis, and Bryan Perozzi · 2024
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