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In many important graph data processing applications the acquired information includes both node features and observations of the graph topology.
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Convolutional neural networks on graphs with fast localized spectral filtering
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Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov · 2016
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Adaptive diffusions for scalable learning over graphs
Dimitris Berberidis, Athanasios Nikolakopoulos, and Georgios B Giannakis · 2018
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Contextual stochastic block models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, and Elchanan Mossel · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
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Dfnets: Spectral cnns for graphs with feedback-looped filters
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Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann · 2020
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Outcome correlation in graph neural network regression
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Graph neural networks exponentially lose expressive power for node classification
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