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Graph neural networks (GNNs) have attracted considerable attention from the research community.
On the runge example
James F Epperson · 1987
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On spectral clustering: Analysis and an algorithm
Andrew Y. Ng, Michael I. Jordan, and Yair Weiss · 2001
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Spectral graph theory and its applications
Daniel A. Spielman · 2007
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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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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Inductive representation learning on large graphs
William L. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 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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Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang · 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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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M. Bronstein · 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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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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Graph wavelet neural network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 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.
A novel graph wavelet model for brain multi-scale activational-connectional feature fusion
Wenyan Xu, Qing Li, Zhiyuan Zhu, and Xia Wu · 2019
Cited alongside, same era.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
Cited alongside, same era.
Spectral temporal graph neural network for multivariate time-series forecasting
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang · 2020
Cited alongside, same era.
Graph signal processing for machine learning: A review and new perspectives
Xiaowen Dong, Dorina Thanou, Laura Toni, Michael M. Bronstein, and Pascal Frossard · 2020
Cited alongside, same era.
A unified view on graph neural networks as graph signal denoising
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah · 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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Magnet: A neural network for directed graphs
Xitong Zhang, Yixuan He, Nathan Brugnone, Michael Perlmutter, and Matthew J. Hirn · 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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Graph neural networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi · 2022
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Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis · 2020
Cited alongside, same era.
Graph signal processing for geometric data and beyond: Theory and applications
Wei Hu, Jiahao Pang, Xianming Liu, Dong Tian, Chia-Wen Lin, and Anthony Vetro · 2020
Cited alongside, same era.
Mathnet: Haar-like wavelet multiresolution-analysis for graph representation and learning
Xuebin Zheng, Bingxin Zhou, Ming Li, Yu Guang Wang, and Junbin Gao · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Breaking the limits of message passing graph neural networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère, Pascal Vasseur, Sébastien Adam, and Paul Honeine · 2021
Cited alongside, same era.
Graph neural networks in network neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, and Islem Rekik · 2021
Cited alongside, same era.
Beyond low-frequency information in graph convolutional networks
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen · 2021
Cited alongside, same era.
Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, and Yizhou Yu · 2022
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Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
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Graph-based molecular representation learning
Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, and Nitesh V. Chawla · 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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Adaptive kernel graph neural network
Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, and Liang Zhao · 2022
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Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks
Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, and Zhewei Wei · 2022
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Sign and basis invariant networks for spectral graph representation learning
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2022
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Point cloud attacks in graph spectral domain: When 3d geometry meets graph signal processing
Daizong Liu, Wei Hu, and Xin Li · 2022
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Revisiting graph contrastive learning from the perspective of graph spectrum
Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, and Jian Pei · 2022
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Multivariate time-series forecasting with temporal polynomial graph neural networks
Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, and Wei Chen · 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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How powerful are spectral graph neural networks
Xiyuan Wang and Muhan Zhang · 2022
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Equivariant and stable positional encoding for more powerful graph neural networks
Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li · 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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Specformer: Spectral graph neural networks meet transformers
Deyu Bo, Chuan Shi, Lele Wang, and Renjie Liao · 2023
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