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Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks.
Strategies for pre-training graph neural networks
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A short introduction to boosting
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
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Protein function prediction via graph kernels
Borgwardt, K. M.; Ong, C. S.; Schönauer, S.; Vishwanathan, S.; Smola, A. J.; and Kriegel, H.-P. 2005 · 2005
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Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J.; Kleinberg, J.; and Faloutsos, C. 2005 · 2005
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On the bottleneck of graph neural networks and its practical implications
Alon, U.; and Yahav, E. 2020 · 2006
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Collective classification in network data
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Multi-class adaboost
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Query-driven active surveying for collective classification
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Adam: A method for stochastic optimization
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Fitnets: Hints for thin deep nets
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A.; Unterthiner, T.; and Hochreiter, S. 2015 · 2015
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; Dean, J.; et al. 2015 · 2015
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Image-based recommendations on styles and substitutes
McAuley, J.; Targett, C.; Shi, Q.; and Van Den Hengel, A. 2015 · 2015
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Deep graph kernels
Yanardag, P.; and Vishwanathan, S. 2015 · 2015
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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
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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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
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Yim, J.; Joo, D.; Bae, J.; and Kim, J. 2017 · 2017
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Revisit knowledge distillation: a teacher-free framework
Yuan, L.; Tay, F. E.; Li, G.; Wang, T.; and Feng, J. 2019 · 2019
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Analyzing the expressive power of graph neural networks in a spectral perspective
Balcilar, M.; Renton, G.; Héroux, P.; Gaüzère, B.; Adam, S.; and Honeine, P. 2020 · 2020
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Tinygnn: Learning efficient graph neural networks
Yan, B.; Wang, C.; Guo, G.; and Lou, Y. 2020 · 2020
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Distilling knowledge from graph convolutional networks
Yang, Y.; Qiu, J.; Song, M.; Tao, D.; and Wang, X. 2020 · 2020
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Reliable data distillation on graph convolutional network
Zhang, W.; Miao, X.; Shao, Y.; Jiang, J.; Chen, L.; Ruas, O.; and Cui, B. 2020 · 2020
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Self-distillation as instance-specific label smoothing
Zhang, Z.; and Sabuncu, M. 2020 · 2020
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Learning from multiple teacher networks
You, S.; Xu, C.; Xu, C.; and Tao, D. 2017 · 2017
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Born again neural networks
Furlanello, T.; Lipton, Z.; Tschannen, M.; Itti, L.; and Anandkumar, A. 2018 · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q.; Han, Z.; and Wu, X.-M. 2018 · 2018
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Pitfalls of graph neural network evaluation
Shchur, O.; Mumme, M.; Bojchevski, A.; and Günnemann, S. 2018 · 2018
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Graph attention networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2018 · 2018
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Fast Graph Representation Learning with PyTorch Geometric
Fey, M.; and Lenssen, J. E. 2019 · 2019
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Graph-Free Knowledge Distillation for Graph Neural Networks
Deng, X.; and Zhang, Z. 2021 · 2021
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Few-shot graph learning for molecular property prediction
Guo, Z.; Zhang, C.; Yu, W.; Herr, J.; Wiest, O.; Jiang, M.; and Chawla, N. V. 2021 · 2021
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Automated self-supervised learning for graphs
Jin, W.; Liu, X.; Zhao, X.; Ma, Y.; Shah, N.; and Tang, J. 2021 · 2021
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Does knowledge distillation really work?
Stanton, S.; Izmailov, P.; Kirichenko, P.; Alemi, A. A.; and Wilson, A. G. 2021 · 2021
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Adagcn: Adaboosting graph convolutional networks into deep models
Sun, K.; Zhu, Z.; and Lin, Z. 2021 · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; and Jégou, H. 2021 · 2021
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Mulde: Multi-teacher knowledge distillation for low-dimensional knowledge graph embeddings
Wang, K.; Liu, Y.; Ma, Q.; and Sheng, Q. Z. 2021 · 2021
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Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework
Yang, C.; Liu, J.; and Shi, C. 2021 · 2021
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FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Feng, K.; Li, C.; Yuan, Y.; and Wang, G. 2022 · 2022
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Zhang, S.; Liu, Y.; Sun, Y.; and Shah, N. 2022 · 2022
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Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Zheng, W.; Huang, E. W.; Rao, N.; Katariya, S.; Wang, Z.; and Subbian, K. 2022 · 2022
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