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English Information

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Authors
# Name
1 Claudio Daniel de Barros(claudiodtbarros@gmail.com)
2 Daniel da Silva(danielnascramos@gmail.com)
3 Fabio Porto(Fporto@lncc.br)

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Reference
# Reference
1 Alon, U. and Yahav, E. (2020). On the bottleneck of graph neural networks and its prac-tical implications.arXiv preprint arXiv:2006.05205.
2 Barros, C. D., Mendonc ̧a, M. R., Vieira, A. B., and Ziviani, A. (2021). A survey on embedding dynamic graphs.arXiv preprint arXiv:2101.01229
3 Bojchevski, A., Klicpera, J., Perozzi, B., Kapoor, A., Blais, M., R ́ozemberczki, B.,Lukasik, M., and G ̈unnemann, S. (2020). Scaling graph neural networks with approx-imate pagerank. InProceedings of the 26th ACM SIGKDD International Conferenceon Knowledge Discovery & Data Mining, pages 2464–2473
4 Cai, H., Zheng, V. W., and Chang, K. C.-C. (2018). A comprehensive survey of graphembedding: Problems, techniques, and applications.IEEE Transactions on Knowledgeand Data Engineering, 30(9):1616–1637
5 Goodfellow, I., Bengio, Y., and Courville, A. (2016).Deep Learning. MIT Press
6 Hamilton, W. L. (2020). Graph representation learning.Synthesis Lectures on ArtificialIntelligence and Machine Learning, 14(3):1–159.
7 Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018).Foundations of Machine Learn-ing. MIT Press.
8 Nickel, M., Murphy, K., Tresp, V., and Gabrilovich, E. (2015). A review of relationalmachine learning for knowledge graphs.Proceedings of the IEEE, 104(1):11–33.
9 Zhang, D., Huang, X., Liu, Z., Zhou, J., Hu, Z., Song, X., Ge, Z., Wang, L., Zhang,Z., and Qi, Y. (2020). Agl: A scalable system for industrial-purpose graph machinelearning.Proc. VLDB Endow., 13(12):3125–3137