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Graph Neural Networks (GNNs) have shown advantages in various graph-based applications.
Understanding the representation power of graph neural networks in learning graph topology
Dehmamy, N.; Barabási, A.-L.; and Yu, R. 2019 · 1907
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Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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Assortative mixing in networks
Newman, M. E. 2002 · 2002
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Geom-gcn: Geometric graph convolutional networks
Pei, H.; Wei, B.; Chang, K. C.-C.; Lei, Y.; and Yang, B. 2020 · 2002
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Graph Random Neural Network for Semi-Supervised Learning on Graphs
Feng, W.; Zhang, J.; Dong, Y.; Han, Y.; Luan, H.; Xu, Q.; Yang, Q.; Kharlamov, E.; and Tang, J. 2020 · 2005
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Non-local graph neural networks
Liu, M.; Wang, Z.; and Ji, S. 2020 · 2005
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Beyond homophily in graph neural networks: Current limitations and effective designs
Zhu, J.; Yan, Y.; Zhao, L.; Heimann, M.; Akoglu, L.; and Koutra, D. 2020 · 2006
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The graph neural network model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2008 · 2008
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Social influence analysis in large-scale networks
Tang, J.; Sun, J.; Wang, C.; and Yang, Z. 2009 · 2009
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Vinyals, O.; Fortunato, M.; and Jaitly, N. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
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Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W. L.; Ying, R.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
Ribeiro, L. F.; Saverese, P. H.; and Figueiredo, D. R. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S.; Perozzi, B.; Kapoor, A.; Alipourfard, N.; Lerman, K.; Harutyunyan, H.; Ver Steeg, G.; and Galstyan, A. 2019 · 2019
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Diffusion improves graph learning
Klicpera, J.; Weißenberger, S.; and Günnemann, S. 2019 · 2019
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Simplifying graph convolutional networks
Wu, F.; Souza, A.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. 2019 · 2019
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Scaling graph neural networks with approximate pagerank
Bojchevski, A.; Klicpera, J.; Perozzi, B.; Kapoor, A.; Blais, M.; Rózemberczki, B.; Lukasik, M.; and Günnemann, S. 2020 · 2020
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Chen, D.; Lin, Y.; Li, W.; Li, P.; Zhou, J.; and Sun, X. 2020 · 2020
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Chen, J.; Ma, T.; and Xiao, C. 2018 · 2018
Cited alongside, same era.
Large-scale learnable graph convolutional networks
Gao, H.; Wang, Z.; and Ji, S. 2018 · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J.; Bojchevski, A.; and Günnemann, S. 2018 · 2018
Cited alongside, same era.
Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q.; Han, Z.; and Wu, X.-M. 2018 · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018 · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Ying, R.; He, R.; Chen, K.; Eksombatchai, P.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
Cited alongside, same era.
Jiang, M.; Liu, G.; Su, Y.; and Wu, X. 2021 · 2021
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Rethinking Graph Transformers with Spectral Attention
Kreuzer, D.; Beaini, D.; Hamilton, W. L.; Létourneau, V.; and Tossou, P. 2021 · 2021
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Multi-scale attributed node embedding
Rozemberczki, B.; Allen, C.; and Sarkar, R. 2021 · 2021
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Deep Constraint-based Propagation in Graph Neural Networks
Tiezzi, M.; Marra, G.; Melacci, S.; and Maggini, M. 2021 · 2021
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Do Transformers Really Perform Bad for Graph Representation?
Ying, C.; Cai, T.; Luo, S.; Zheng, S.; Ke, G.; He, D.; Shen, Y.; and Liu, T.-Y. 2021 · 2021
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Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Yuan, H.; and Ji, S. 2021 · 2021
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