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Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data.
The nonstochastic multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire · 2002
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Introductory lectures on convex optimization: A basic course , volume 87
Y. Nesterov · 2003
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2005
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Algorithms for adversarial bandit problems with multiple plays
T. Uchiya, A. Nakamura, and M. Kudo · 2010
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
S. Bubeck and N. Cesa-Bianchi · 2012
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Stochastic multi-armed-bandit problem with non-stationary rewards
O. Besbes, Y. Gur, and A. Zeevi · 2014
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Discriminative embeddings of latent variable models for structured data
H. Dai, B. Dai, and L. Song · 2016
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Stochastic variance reduction for nonconvex optimization
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola · 2016
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Graph convolutional matrix completion
R. v. d. Berg, T. N. Kipf, and M. Welling · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
A. Bojchevski and S. Günnemann · 2017
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
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Protein interface prediction using graph convolutional networks
A. Fout, J. Byrd, B. Shariat, and A. Ben-Hur · 2017
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Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Stochastic optimization with bandit sampling
F. Salehi, L. E. Celis, and P. Thiran · 2017
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P. Veličković, 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
Cited alongside, same era.
Spam review detection with graph convolutional networks
A. Li, Z. Qin, R. Liu, Y. Yang, and D. Li · 2019
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Advancing graphsage with a data-driven node sampling
J. Oh, K. Cho, and J. Bruna · 2019
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Heterogeneous graph neural network
C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla · 2019
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Layer-dependent importance sampling for training deep and large graph convolutional networks
D. Zou, Z. Hu, Y. Wang, S. Jiang, Y. Sun, and Q. Gu · 2019
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Minimal variance sampling with provable guarantees for fast training of graph neural networks
W. Cong, R. Forsati, M. Kandemir, and M. Mahdavi · 2020
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Heterogeneous graph transformer
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Contextual stochastic block models
Y. Deshpande, S. Sen, A. Montanari, and E. Mossel · 2018
Cited alongside, same era.
Adaptive sampling towards fast graph representation learning
W. Huang, T. Zhang, Y. Rong, and J. Huang · 2018
Cited alongside, same era.
Heterogeneous graph neural networks for malicious account detection
Z. Liu, C. Chen, X. Yang, J. Zhou, X. Li, and L. Song · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
Cited alongside, same era.
R. Ge, S. M. Kakade, R. Kidambi, and P. Netrapalli · 2019
Cited alongside, same era.
Z. Hu, Y. Dong, K. Wang, and Y. Sun · 2020
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Bandit samplers for training graph neural networks
Z. Liu, Z. Wu, Z. Zhang, J. Zhou, S. Yang, L. Song, and Y. Qi · 2020
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Geom-gcn: Geometric graph convolutional networks
H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
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GraphSAINT: Graph sampling based inductive learning method
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna · 2020
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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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