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Graph-based learning is a rapidly growing sub-field of machine learning with applications in social networks, citation networks, and bioinformatics.
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Spectral networks and locally connected networks on graphs
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Neural machine translation by jointly learning to align and translate
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Convolutional networks on graphs for learning molecular fingerprints
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Diffusion-convolutional neural networks
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Gated graph sequence neural networks
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Covariate-assisted spectral clustering
N. Binkiewicz, J. T. Vogelstein, and K. Rohe · 2017
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Inductive representation learning on large graphs
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Semi-supervised classification with graph convolutional networks
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The computer science and physics of community detection: Landscapes, phase transitions, and hardness
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Attention is all you need
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Community detection and stochastic block models: Recent developments
E. Abbe · 2018
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Statistical inference on random dot product graphs: A survey
A. Athreya, D. E. Fishkind, M. Tang, C. E. Priebe, Y. Park, J. T. Vogelstein, K. Levin, V. Lyzinski, Y. Qin, and D. L. Sussman · 2018
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X. Bresson and T. Laurent · 2018
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Contextual stochastic block models
Y. Deshpande, A. Montanari S. Sen, and E. Mossel · 2018
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Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Ron Levie, Federico Monti, Xavier Bresson, and Michael M Bronstein · 2018
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
How hard is to distinguish graphs with graph neural networks?
A. Loukas · 2020
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What graph neural networks cannot learn: Depth vs width
A. Loukas · 2020
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Global attention improves graph networks generalization
O. Puny, H. Ben-Hamu, and Y. Lipman · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
A. Baranwal, K. Fountoulakis, and A. Jagannath · 2021
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High-dimensional probability: An introduction with applications in data science
R. Vershynin · 2018
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Supervised community detection with line graph neural networks
Z. Chen, L. Li, and J. Bruna · 2019
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Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
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Measuring and improving the use of graph information in graph neural networks
Y. Hou, J. Zhang, J. Cheng, K. Ma, R. T. B. Ma, H. Chen, and M.-C. Yang · 2019
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Understanding attention and generalization in graph neural networks
B. Knyazev, G. W. Taylor, and M. Amer · 2019
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Attention models in graphs: A survey
B. J. Lee, R. A. Rossi, S. Kim, K. N. Ahmed, and E. Koh · 2019
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Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Adaptive universal generalized pagerank graph neural network
E. Chien, J. Peng, P. Li, and O. Milenkovic · 2021
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On the universality of graph neural networks on large random graphs
N. Keriven, A. Bietti, and S. Vaiter · 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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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lio, and Michael Bronstein · 2022
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How attentive are graph attention networks
S. Brody, U. Alon, and E. Yahav · 2022
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Theory of graph neural networks: Representation and learning
S. Jegelka · 2022
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Revisiting heterophily for graph neural networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2022
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Generalization analysis of message passing neural networks on large random graphs
S. Maskey, R. Levie, Y. Lee, and G. Kutyniok · 2022
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Graphworld: Fake graphs bring real insights for gnns
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi · 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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