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Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption).
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Birds of a feather: Homophily in social networks
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An adaptive filter-bank equalizer for speech enhancement
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The graph neural network model
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Imagenet classification with deep convolutional neural networks
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Speech recognition with deep recurrent neural networks
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Neural machine translation by jointly learning to align and translate
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Graph structured data viewed through a fourier lens
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Adam: A method for stochastic optimization
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Deep learning
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Geometric deep learning: going beyond euclidean data
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Convolutional neural networks on graphs with fast localized spectral filtering
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Semi-supervised classification with graph convolutional networks
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Inductive representation learning on large graphs
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P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
J. Klicpera, A. Bojchevski, and S. Günnemann · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan · 2019
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
B. Rozemberczki and R. Sarkar · 2020
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Graph neural networks with heterophily
J. Zhu, R. A. Rossi, A. Rao, T. Mai, N. Lipka, N. K. Ahmed, and D. Koutra · 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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Beyond low-frequency information in graph convolutional networks
D. Bo, X. Wang, C. Shi, and H. Shen · 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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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. Ma, H. Chen, and M.-C. Yang · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
S. Luan, M. Zhao, X.-W. Chang, and D. Precup · 2019
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Revisiting graph neural networks: All we have is low-pass filters
T. Maehara · 2019
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Simplifying graph convolutional networks
F. Wu, T. Zhang, A. H. d. Souza Jr, C. Fifty, T. Yu, and K. Q. Weinberger · 2019
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Simple and deep graph convolutional networks
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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Graph representation learning
W. L. Hamilton · 2020
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
D. Lim, F. Hohne, X. Li, S. L. Huang, V. Gupta, O. Bhalerao, and S. N. Lim · 2021
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New benchmarks for learning on non-homophilous graphs
D. Lim, X. Li, F. Hohne, and S.-N. Lim · 2021
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Simple truncated svd based model for node classification on heterophilic graphs
V. Lingam, R. Ragesh, A. Iyer, and S. Sellamanickam · 2021
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Non-local graph neural networks
M. Liu, Z. Wang, and S. Ji · 2021
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Is homophily a necessity for graph neural networks?
Y. Ma, X. Liu, N. Shah, and J. Tang · 2021
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Multi-Scale Attributed Node Embedding
B. Rozemberczki, C. Allen, and R. Sarkar · 2021
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Y. Yan, M. Hashemi, K. Swersky, Y. Yang, and D. Koutra · 2021
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
C. Bodnar, F. Di Giovanni, B. P. Chamberlain, P. Liò, and M. M. Bronstein · 2022
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Finding global homophily in graph neural networks when meeting heterophily
X. Li, R. Zhu, Y. Cheng, C. Shan, S. Luo, D. Li, and W. Qian · 2022
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