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Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth.
Encoding labeled graphs by labeling RAAM
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Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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H Nt and T Maehara · 2019
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Traffic Prediction with Graph Neural Networks in Google Maps
Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Peter W Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Veličković · 2021
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Graph neural networks: a review of methods and applications
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Towards deeper graph neural networks
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Graph neural networks exponentially lose expressive power for node classification
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Utilizing graph machine learning within drug discovery and development
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
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Andreea Deac, Marc Lackenby, and Petar Veličković · 2022
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Graph neural networks as gradient flows
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Not too little, not too much: a theoretical analysis of graph (over) smoothing
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Graph-coupled oscillator networks
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Acmp: Allen-cahn message passing for graph neural networks with particle phase transition
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Gradient gating for deep multi-rate learning on graphs
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