Abstract
Through mapping network nodes into low-dimensional vectors, network embedding methods have shown promising results for many downstream tasks, such as link prediction and node classification. Recently, attributed network embedding obtained progress on the network associated with node attributes. However, it is insufficient to ignore the attributes of the context nodes, which are also helpful for node proximity. In this paper, we propose a new attributed network embedding method named PCANE (Preserving Context Attributes for Network Embedding). PCANE preserves both network structure and the context attributes by optimizing new object functions, and further produces more informative node representations. PCANE++ is also proposed to represent the isolated nodes, and is better to represent high degree nodes. Experiments on 3 real-world attributed networks show that our methods outperform the other network embedding methods on link prediction and node classification tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
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. ACM (2016)
Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 633–641. SIAM (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)
Levy, O., Goldberg, Y., Dagan, I.: Improving distributional similarity with lessons learned from word embeddings. Bulletin De La Socit Botanique De France 75(3), 552–555 (2015)
Liang, J., Jacobs, P., Sun, J., Parthasarathy, S.: Semi-supervised embedding in attributed networks with outliers. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 153–161. SIAM (2018)
Liao, L., He, X., Zhang, H., Chua, T.S.: Attributed social network embedding. IEEE Trans. Knowl. Data Eng. 30, 2257–2270 (2018). (Early access)
Liao, L., Ho, Q., Jiang, J., Lim, E.P.: SLR: a scalable latent role model for attribute completion and tie prediction in social networks. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE, pp. 1062–1073. IEEE (2016)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: International Conference on Neural Information Processing Systems, pp. 3111–3119 (2013)
Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: International Joint Conference on Artificial Intelligence, pp. 1895–1901 (2016)
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
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, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)
Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of facebook networks. Phys. A: Stat. Mech. Appl. 391(16), 4165–4180 (2012). Social Science Electronic Publishing
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI, pp. 2111–2117 (2015)
Zhang, D., Yin, J., Zhu, X., Zhang, C.: User profile preserving social network embedding. In: Proceedings of IJCAI, pp. 3378–3384 (2017)
Zou, K., O’Malley, A.J., Mauri, L.: Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115(5), 654–657 (2007)
Acknowledgement
This work is sponsored, in part, by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant number 18KJB510010 and National Nature Science Foundation of China (NSFC) under grant number 61472183.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, D., Dai, Xy., Yang, K., Chen, J., He, Y. (2019). PCANE: Preserving Context Attributes for Network Embedding. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-16142-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16141-5
Online ISBN: 978-3-030-16142-2
eBook Packages: Computer ScienceComputer Science (R0)