Self-supervised Training of Graph Convolutional Networks

J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 1993–2001

2001

Earlier work this paper cites.

X. Zhu, Z. Ghahramani, and J. D. Lafferty, “Semi-supervised learning using gaussian fields and harmonic functions,” in Proceedings of the 20th International conference on Machine learning (ICML) , 2003, pp. 912–919

2003

Earlier work this paper cites.

Q. Lu and L. Getoor, “Link-based classification,” in Proceedings of the 20th International Conference on Machine Learning (ICML) , 2003, pp. 496–503

2003

Earlier work this paper cites.

M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: A geometric framework for learning from labeled and unlabeled examples,” Journal of machine learning research , vol. 7, no. Nov, pp. 2399–2434, 2006

2006

Earlier work this paper cites.

L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research , vol. 9, no. 11, pp. 2579–2605, 2008

2008

Earlier work this paper cites.

J. Weston, F. Ratle, H. Mobahi, and R. Collobert, “Deep learning via semi-supervised embedding,” in Neural Networks: Tricks of the Trade . Springer, 2012, pp. 639–655

2012

Earlier work this paper cites.

J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and locally connected networks on graphs,” arXiv preprint arXiv:1312.6203 , 2013

Original

2013

Earlier work this paper cites.

B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2014, pp. 701–710

2014

Earlier work this paper cites.

D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in Neural Information Processing Systems , 2015, pp. 2224–2232

2015

Cited alongside, same era.

C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1422–1430

2015

Cited alongside, same era.

F. Chollet, “Keras,” https://keras.io

2015

Cited alongside, same era.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016

Original

2016

Cited alongside, same era.

Z. Yang, W. W. Cohen, and R. Salakhutdinov, “Revisiting semi-supervised learning with graph embeddings,” International Conference on Machine Learning , pp. 40–48, 2016

K. Marino, R. Salakhutdinov, and A. Gupta, “The more you know: Using knowledge graphs for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2673–2681

2017

Later among the works it cites.

S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker, and D. Rueckert, “Spectral graph convolutions for population-based disease prediction,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 177–185

2017

Later among the works it cites.

P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in Proceedings of the International Conference on Learning Representations , 2017

2017

Later among the works it cites.

F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5115–5124