H., Tong, H., Yang, J., He, Q., & Chen, B. C. (2017). Nemo: Next career move prediction with contextual embedding. In Proceedings of the 26th International Conference on World Wide Web Companion. 505‒513. 2. Linmei, H., Yang, T., Shi, C., Ji, H., & Li, X. (2019). Heterogeneous graph attention networks for semi-supervised short text classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 4821‒4830. 3. Liu, J., Ng, Y. C., Wood, K. L., & Lim, K. H. (2020). IPOD: A Large-scale Industrial and Professional Occupation Dataset. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing. 323‒328. 4. Meng, Q., Zhu, H., Xiao, K., Zhang, L., & Xiong, H. (2019). A hierarchical career-path-aware neural network for job mobility prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 14‒24. 5. Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. In The World Wide Web Conference. 2022‒2032. 6. Zhang, L., Zhou, D., Zhu, H., Xu, T., Zha, R., Chen, E., & Xiong, H. (2021). Attentive heterogeneous graph embedding for job mobility prediction. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2192‒2201.