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Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Gradient-based learning applied to document recognition. In Proceedings of the IEEE
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. 1998 · 1998
Earlier work this paper cites.
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. In Neural Computation
Mikhail Belkin and Partha Niyogi. 2003 · 2003
Earlier work this paper cites.
Link-based classification. In International Conference on Machine Learning (ICML)
Qing Lu and Lise Getoor. 2003 · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions. In International Conference on Machine Learning (ICML)
Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. 2003 · 2003
Earlier work this paper cites.
Wavelets on graphs via spectral graph theory. In Applied and Computational Harmonic Analysis
David K. Hammond, Pierre Vandergheynst, and R. Gribonval. 2011 · 2011
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Deeplearning via semi-supervised embedding. In Neural Networks: Tricks of the Trade . 639–655
Jason Weston, Frederic Ratle, Hossein Mobahi, and Ronan Collobert. 2012 · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013 · 2013
Earlier work this paper cites.
Spectral Networks and Locally Connected Networks on Graphs. In International Conference on Learning Representations
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun. 2014 · 2014
Cited alongside, same era.
Glove: Global Vectors for Word Representation. In Conference on Empirical Methods in Natural Language Processing, EMNLP
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
DeepWalk: Online Learning of Social Representations. In Knowledge Discovery and Data Mining
B. Perozzi, R. Al-Rfou, and S. Skiena. 2014 · 2014
Cited alongside, same era.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015 · 2015
Cited alongside, same era.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems (NIPS)
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Later among the works it cites.
node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
A. Grover and J. Leskovec. 2016 · 2016
Later among the works it cites.
Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Later among the works it cites.
Revisiting Semi-Supervised Learning with Graph Embeddings. In International Conference on Machine Learning (ICML)
Z. Yang, W. Cohen, and R. Salakhutdinov. 2016 · 2016
Later among the works it cites.
Watch Your Step: Learning Graph Embeddings Through Attention. In arxiv
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, and Alex Alemi. 2017 · 2017
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Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations
Jimmy Ba and Diederik Kingma. 2015 · 2015
Cited alongside, same era.
Improving Distributional Similarity with Lessons Learned from Word Embeddings. In Transactions of the Association for Computational Linguistics (TACL)
Omer Levy, Yoav Goldberg, and Ido Dagan. 2015 · 2015
Cited alongside, same era.
Going Deeper with Convolutions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Cited alongside, same era.
Diffusion-Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NIPS)
James Atwood and Don Towsley. 2016 · 2016
Cited alongside, same era.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. In Journal of machine learning research (JMLR)
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 2006a
Cited in the paper.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. In Journal of machine learning research (JMLR)
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 2006b
Cited in the paper.
Later among the works it cites.
Inductive Representation Learning on Large Graphs. In NIPS
W. Hamilton, R. Ying, and J. Leskovec. 2017 · 2017
Later among the works it cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
T. Kipf and M. Welling. 2017 · 2017
Later among the works it cites.
Data Analytics for Optimal Detection of Metastatic Prostate Cancer. (2017)
Selin Merdan, Christine L. Barnett, and Brian T. Denton. 2017 · 2017
Later among the works it cites.