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The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms.
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Aleksandar Bojchevski and Stephan Günnemann · 2017
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Michaël Fanuel, Carlos M Alaiz, and Johan AK Suykens · 2017
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Thomas N. Kipf and Max Welling · 2017
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Yi Ma, Jianye Hao, Yaodong Yang, Han Li, Junqi Jin, and Guangyong Chen · 2019
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Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2019
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Alexandre Bovet and Peter Grindrod · 2020
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Hermitian matrices for clustering directed graphs: insights and applications
Mihai Cucuringu, Huan Li, He Sun, and Luca Zanetti · 2020
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Characterization and comparison of large directed networks through the spectra of the magnetic laplacian
Bruno Messias F. de Resende and Luciano da F. Costa · 2020
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Magnetic eigenmaps for the visualization of directed networks
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Federico Monti, Karl Otness, and Michael M. Bronstein · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Graph signal processing for directed graphs based on the hermitian laplacian
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Signal processing on directed graphs: The role of edge directionality when processing and learning from network data
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Péter Mernyei and Cătălina Cangea · 2020
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A new kind of hermitian matrices for digraphs
Bojan Mohar · 2020
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Geom-gcn: Geometric graph convolutional networks
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Digraph inception convolutional networks
Z. Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David S. Rosenblum, and A. Lim · 2020
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Directed graph convolutional network
Zekun Tong, Yuxuan Liang, Changsheng Sun, David S. Rosenblum, and Andrew Lim · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2020
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Spectral clustering for directed networks
Palmer W.R. and Zheng T · 2021
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