1. Nayyeri, M., Xu, C., Hoffmann, F., Vahdati, S.: Knowledge graph representation learning using ordinary differential equations. EMNLP. 14(6), 641–648 (2021)
2. Zhu, D., Zeng, W., Su, J.: Construction of transformer substation fault knowledge graph based on a depth learning algorithm. Int. J. Model. Simul. Sci. Comput. 14(01), 2341017 (2023). https://doi.org/10.1142/S1793962323410179
3. Chen, L., Cui, J., Tang, X., Song, C. , Qian, Y., Li, Y., et al.: RMNA: a neighbor aggregation-based knowledge graph representation learning model using rule mining. arXiv 2(3)5–12 (2021)
4. Zhang, J., Liang, S., Shao, J., Sheng, Y.: Temporal knowledge graph representation learning with local and global evolutions. Knowl. Based Syst. 21(1), 1–28 (2022)
5. Hai-Cheng, Y., Zhu-Hong, Y., De-Shuang, H., Keong, K.C.: Graph representation learning in bioinformatics: trends, methods and applications. Brief. Bioinform. 20(7), 752–766 (2021)