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The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
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Algebraic connectivity of graphs
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Vishwaraj Doshi and Do Young Eun · 2000
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The principal components analysis of a graph, and its relationships to spectral clustering
Marco Saerens, Francois Fouss, Luh Yen, and Pierre Dupont · 2004
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The finer directional wavelet transform
Yue Lu and Minh N. Do · 2005
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Laplace-beltrami eigenfunctions towards an algorithm that "understands" geometry
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Alex Krizhevsky, 2009
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Discrete calculus : applied analysis on graphs for computational science
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Geometrical structure of laplacian eigenfunctions
D. S. Grebenkov and B.-T. Nguyen · 2013
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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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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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Random Walks and Heat Kernels on Graphs
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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
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Boris Knyazev, Graham W Taylor, and Mohamed Amer · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, and Doina Precup · 2019
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Sarah O’Gara and Kevin McGuinness · 2019
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Graph convolutional neural networks via motif-based attention
Hao Peng, Jianxin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, and Philip S. Yu · 2019
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Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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On the bottleneck of graph neural networks and its practical implications
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