Abstract
Recently, the heterogeneous network embedding (HNE for short) methods have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, the rich node label information is not considered by these HNE methods, which leads to suboptimal node embeddings. In this paper, we propose a novel Label Preserved Heterogeneous Network Embedding (LPHNE) method to tackle this problem. Briefly, for each type of the nodes, LPHNE projects these nodes and their labels into a same low-dimensional hidden space by modeling the interactive relationship between the labels and the contexts of the nodes. Thus, the discriminability of node embedding is improved by utilizing the label information. The extensive experimental results demonstrate that our semi-supervised method outperforms the various competitive baselines on two widely used network datasets significantly.
W. Chen—Independent Researcher.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: SIGKDD, pp. 119–128. ACM (2015)
Chen, W., Zhang, X., Wang, J., Zhang, Y., Yan, H., Li, X.: Non-linear smoothed transductive network embedding with text information. In: ACML, pp. 1–16 (2016)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: SIGKDD, KDD ’16, pp. 855–864. ACM, New York, NY, USA (2016)
Jacob, Y., Denoyer, L., Gallinari, P.: Learning latent representations of nodes for classifying in heterogeneous social networks. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp 373–382. ACM (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014)
Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: Association for Computational Linguistics (ACL), pp. 1004–1013 (2016)
Linmei, H., Yang, T., Shi, C., Ji, H., Li, X.: 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), pp. 4821–4830 (2019)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Shang, J., Qu, M., Liu, J., Kaplan, L.M., Han, J., Peng, J.: Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. arXiv preprint arXiv:1610.09769 (2016)
Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2018)
Tang, J., Qu, M., Mei, Q.: Pte: Predictive text embedding through large-scale heterogeneous text networks. In: SIGKDD, pp. 1165–1174. ACM (2015)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pp. 1067–1077. ACM, New York, NY, USA (2015)
Tang, L., Liu, Z., Zhao, Y., Duan, Z., Jia, J.: Efficient ridesharing framework for ride-matching via heterogeneous network embedding. ACM Trans. Knowl. Discovery Data (TKDD) 14(3), 1–24 (2020)
Tu, C., Zhang, W., Liu, Z., Sun, M.: max-margin deepwalk: discriminative learning of network representation. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 3889–3895 (2016)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pp. 1225–1234. ACM, New York, NY, USA (2016)
Xu, L., Wei, X., Cao, J., Yu, P.S.: Embedding of embedding (eoe): Joint embedding for coupled heterogeneous networks. In: WSDM, WSDM ’17, pp. 741–749. ACM, New York, NY, USA (2017)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pp. 2111–2117. AAAI Press (2015)
Acknowledgments
This work is supported by Natural Science Foundation of China [62003028]. We thank the anonymous reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Chen, W. (2021). Label Preserved Heterogeneous Network Embedding. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13109. Springer, Cham. https://doi.org/10.1007/978-3-030-92270-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-92270-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92269-6
Online ISBN: 978-3-030-92270-2
eBook Packages: Computer ScienceComputer Science (R0)