Publisher
Springer Nature Singapore
Reference17 articles.
1. Chen, J., Zhu, J., Song, L.: Stochastic training of graph convolutional networks with variance reduction. arXiv preprint arXiv:1710.10568 (2017)
2. Chen, J., Ma, T., Xiao, C.: Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)
3. Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-gcn: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257–266 (2019)
4. Cong, W., Forsati, R., Kandemir, M., Mahdavi, M.: Minimal variance sampling with provable guarantees for fast training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1393–1403 (2020)
5. Etikan, I., Bala, K.: Sampling and sampling methods. Biometrics Biostatistics Int. J. 5(6), 00149 (2017)