Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs.
Journal of the Royal Statistical Society: Series B (Methodological) 26
Box, G.E., Cox, D.R.: An analysis of transformations · 1964
Earlier work this paper cites.
Computer 21
Linsker, R.: Self-organization in a perceptual network · 1988
Earlier work this paper cites.
Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models (2005)
2005
Earlier work this paper cites.
In: Workshop on challenges in representation learning, ICML, vol. 3 (2013)
Lee, D.H., et al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks · 2013
Earlier work this paper cites.
In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 701–710 (2014)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations · 2014
Earlier work this paper cites.
In: Advances in Neural Information Processing Systems, pp. 10–18 (2015)
Van Rooyen, B., Menon, A., Williamson, R.C.: Learning with symmetric label noise: The importance of being unhinged · 2015
Earlier work this paper cites.
arXiv preprint arXiv:1609.02907 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks · 2016
Earlier work this paper cites.
arXiv preprint arXiv:1606.00709 (2016)
Nowozin, S., Cseke, B., Tomioka, R.: f-gan: Training generative neural samplers using variational divergence minimization · 2016
Earlier work this paper cites.
arXiv preprint arXiv:1707.03815 (2017)
Bojchevski, A., Günnemann, S.: Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1706.02216 (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1703.05175 (2017)
Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning · 2017
Earlier work this paper cites.
International Conference on Learning Representations (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks · 2017
Earlier work this paper cites.
In: International Conference on Machine Learning, pp. 531–540. PMLR (2018)
Belghazi, M.I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., Hjelm, D.: Mutual information neural estimation · 2018
Earlier work this paper cites.
arXiv preprint arXiv:1801.10247 (2018)
Chen, J., Ma, T., Xiao, C.: Fastgcn: fast learning with graph convolutional networks via importance sampling · 2018
Earlier work this paper cites.
arXiv preprint arXiv:1808.06670 (2018)
Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.: Learning deep representations by mutual information estimation and maximization · 2018
Cited alongside, same era.
AAAI (2018)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning · 2018
Cited alongside, same era.
arXiv preprint arXiv:1811.05868 (2018)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation · 2018
Cited alongside, same era.
In: Advances in neural information processing systems, pp. 8778–8788 (2018)
Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels · 2018
Cited alongside, same era.
In: international conference on machine learning, pp. 2083–2092. PMLR (2019)
Gao, H., Ji, S.: Graph u-nets · 2019
Cited alongside, same era.
arXiv preprint arXiv:2006.07889 (2020)
Huang, K., Zitnik, M.: Graph meta learning via local subgraphs · 2020
Later among the works it cites.
arXiv preprint arXiv:2007.02901 (2020)
Mernyei, P., Cangea, C.: Wiki-cs: A wikipedia-based benchmark for graph neural networks · 2020
Later among the works it cites.
arXiv preprint arXiv:2006.15315 (2020)
Mukherjee, S., Awadallah, A.H.: Uncertainty-aware self-training for text classification with few labels · 2020
Later among the works it cites.
In: Proceedings of The Web Conference 2020, pp. 259–270 (2020)
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., Huang, J.: Graph representation learning via graphical mutual information maximization · 2020
Later among the works it cites.
In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150–1160 (2020)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
In: International conference on machine learning, pp. 5241–5250. PMLR (2019)
Qu, M., Bengio, Y., Tang, J.: Gmnn: Graph markov neural networks · 2019
Cited alongside, same era.
In: ICLR (Poster) (2019)
Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax · 2019
Cited alongside, same era.
arXiv preprint arXiv:1909.11715 (2019)
Verma, V., Qu, M., Lamb, A., Bengio, Y., Kannala, J., Tang, J.: Graphmix: Regularized training of graph neural networks for semi-supervised learning · 2019
Cited alongside, same era.
In: International conference on machine learning, pp. 6861–6871. PMLR (2019)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks · 2019
Cited alongside, same era.
arXiv preprint arXiv:1910.02684 (2019)
Zhou, Z., Zhang, S., Huang, Z.: Dynamic self-training framework for graph convolutional networks · 2019
Cited alongside, same era.
In: International Conference on Machine Learning, pp. 1725–1735. PMLR (2020)
Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks · 2020
Cited alongside, same era.
In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 295–304 (2020)
Ding, K., Wang, J., Li, J., Shu, K., Liu, C., Liu, H.: Graph prototypical networks for few-shot learning on attributed networks · 2020
Cited alongside, same era.
Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., Tang, J.: Gcc: Graph contrastive coding for graph neural network pre-training · 2020
Later among the works it cites.
In: AAAI (2020)
Sun, K., Zhu, Z., Lin, Z.: Multi-stage self-supervised learning for graph convolutional networks · 2020
Later among the works it cites.
ACM Computing Surveys (CSUR) 53
Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: A survey on few-shot learning · 2020
Later among the works it cites.
Advances in Neural Information Processing Systems 33
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations · 2020
Later among the works it cites.
You, Y., Chen, T., Wang, Z., Shen, Y.: When does self-supervision help graph convolutional networks? · 2020
Later among the works it cites.
arXiv preprint arXiv:2006.04131 (2020)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning · 2020
Later among the works it cites.
In: ICLR (2021)
Kim, D., Oh, A.H.: How to find your friendly neighborhood: Graph attention design with self-supervision · 2021
Later among the works it cites.
Liu, Y., Pan, S., Jin, M., Zhou, C., Xia, F., Yu, P.S.: Graph self-supervised learning: A survey · 2021
Later among the works it cites.
Future Generation Computer Systems 115
Zhan, K., Niu, C.: Mutual teaching for graph convolutional networks · 2021
Later among the works it cites.