Publisher
Springer Nature Singapore
Reference15 articles.
1. Donnat, C., Zitnik, M., Hallac, D., Leskovec, J.: Learning structural node embeddings via diffusion wavelets. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1320–1329. KDD ’18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3220025
2. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Chia Laguna Resort, Sardinia, Italy (2010). https://proceedings.mlr.press/v9/glorot10a.html
3. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. KDD ’16, Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939754
4. Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 731–739. WSDM ’17, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3018661.3018667
5. Kumar, V., Pujari, A.K., Padmanabhan, V., Sahu, S.K., Kagita, V.R.: Multilabel classification using hierarchical embedding. Expert Syst. Appl. 91, 263–269 (2018). https://doi.org/10.1016/j.eswa.2017.09.020, https://www.sciencedirect.com/science/article/pii/S0957417417306309