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Graph Neural Networks (GNNs) have emerged as one of the leading approaches for machine learning on graph-structured data.
Spectral graph theory
Fan RK Chung · 1997
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Stochastic analysis on manifolds
Elton P Hsu · 2002
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Graph wavelets for spatial traffic analysis
Mark Crovella and Eric Kolaczyk · 2003
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Diffusion polynomial frames on metric measure spaces
Mauro Maggioni and Hrushikesh N Mhaskar · 2008
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A concise and provably informative multi-scale signature based on heat diffusion
Jian Sun, Maks Ovsjanikov, and Leonidas Guibas · 2009
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Ollivier-ricci curvature and the spectrum of the normalized graph laplace operator
Frank Bauer, Jürgen Jost, and Shiping Liu · 2011
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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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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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Sparse representation on graphs by tight wavelet frames and applications
Bin Dong · 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 attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graph wavelet neural network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 2018
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Tight framelets on graphs for multiscale data analysis
Yu Guang Wang and Xiaosheng Zhuang · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2020
Cited alongside, same era.
A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang · 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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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Cited alongside, same era.
Generalizing graph convolutional networks via heat kernel
Jialin Zhao, Yuxiao Dong, Jie Tang, Ming Ding, and Kuansan Wang · 2020
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p p -laplacian based graph neural networks
Guoji Fu, Peilin Zhao, and Yatao Bian · 2022
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Understanding the relationship between over-smoothing and over-squashing in graph neural networks
Jhony H Giraldo, Fragkiskos D Malliaros, and Thierry Bouwmans · 2022
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Generalized energy and gradient flow via graph framelets
Andi Han, Dai Shi, Zhiqi Shao, and Junbin Gao · 2022
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Generalized Laplacian regularized framelet gcns
Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, and Junbin Gao · 2022
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On the expressive equivalence between graph convolution and attention models
Dai Shi, Andi Han, Junbin Gao, Yi Guo, et al · 2022
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Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Cited alongside, same era.
Grand: Graph neural diffusion
Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, and Emanuele Rossi · 2021
Cited alongside, same era.
Beltrami flow and neural diffusion on graphs
Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, and Michael Bronstein · 2021
Cited alongside, same era.
A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip · 2021
Cited alongside, same era.
Grand++: Graph neural diffusion with a source term
Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea Bertozzi, Stanley Osher, and Bao Wang · 2021
Cited alongside, same era.
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2021
Cited alongside, same era.
A new perspective on" how graph neural networks go beyond weisfeiler-lehman?"
Asiri Wijesinghe and Qing Wang · 2021
Cited alongside, same era.
GRAND++: Graph neural diffusion with a source term
Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea Bertozzi, Stanley Osher, and Bao Wang · 2022
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Quasi-framelets: Another improvement to graph neural networks
Mengxi Yang, Xuebin Zheng, Jie Yin, and Junbin Gao · 2022
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Decimated framelet system on graphs and fast g-framelet transforms
Xuebin Zheng, Bingxin Zhou, Yu Guang Wang, and Xiaosheng Zhuang · 2022
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A simple yet effective SVD-GCN for directed graphs
Chunya Zou, Andi Han, Lequan Lin, and Junbin Gao · 2022
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Gread: Graph neural reaction-diffusion networks
Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho · 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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A magnetic framelet-based convolutional neural network for directed graphs
Lequan Lin and Junbin Gao · 2023
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Xinliang Liu, Bingxin Zhou, Chutian Zhang, and Yu Guang Wang · 2023
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How curvature enhance the adaptation power of framelet gcns
Dai Shi, Yi Guo, Zhiqi Shao, and Junbin Gao · 2023
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Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao, and Junbin Gao · 2023
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