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Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Fourier analysis: an introduction , volume 1
Elias M Stein and Rami Shakarchi · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Spectral networks and deep locally connected networks on graphs
Joan Bruna Estrach, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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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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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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Graph signal processing: Overview, challenges, and applications
Antonio Ortega, Pascal Frossard, Jelena Kovacevic, José M. F. Moura, and Pierre Vandergheynst · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S. Zemel · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 2019
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Graph signal processing for machine learning: A review and new perspectives
Xiaowen Dong, Dorina Thanou, Laura Toni, Michael M. Bronstein, and Pascal Frossard · 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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OGB-LSC: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Deep Learning on Graphs: Theory, Models, Algorithms and Applications
Renjie Liao · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim · 2021
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Multi-Scale Attributed Node Embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2021
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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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Error bounds for deep relu networks using the kolmogorov-arnold superposition theorem
Hadrien Montanelli and Haizhao Yang · 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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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Beyond low-frequency information in graph convolutional networks
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen · 2021
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A note on sparse generalized eigenvalue problem
Yunfeng Cai, Guanhua Fang, and Ping Li · 2021
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Interpreting and unifying graph neural networks with an optimization framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui · 2021
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How expressive are transformers in spectral domain for graphs?
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, and Isaiah Onando Mulang’ · 2022
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Convolutional neural networks on graphs with chebyshev approximation, revisited
Mingguo He, Zhewei Wei, and Ji-Rong Wen · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampásek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Revisiting over-smoothing in BERT from the perspective of graph
Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, and James T. Kwok · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2022
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Peihao Wang, Wenqing Zheng, Tianlong Chen, and Zhangyang Wang · 2022
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How powerful are spectral graph neural networks
Xiyuan Wang and Muhan Zhang · 2022
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A new perspective on the effects of spectrum in graph neural networks
Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, and Baocai Yin · 2022
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Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2022
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