PCANE: Preserving Context Attributes for Network Embedding

  • Danhao Zhu
  • Xin-yu DaiEmail author
  • Kaijia Yang
  • Jiajun Chen
  • Yong He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)


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 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.


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Liao, L., He, X., Zhang, H., Chua, T.S.: Attributed social network embedding. IEEE Trans. Knowl. Data Eng. 30, 2257–2270 (2018). (Early access)CrossRefGoogle Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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 PublishingCrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI, pp. 2111–2117 (2015)Google Scholar
  17. 17.
    Zhang, D., Yin, J., Zhu, X., Zhang, C.: User profile preserving social network embedding. In: Proceedings of IJCAI, pp. 3378–3384 (2017)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Danhao Zhu
    • 1
    • 2
  • Xin-yu Dai
    • 1
    Email author
  • Kaijia Yang
    • 1
  • Jiajun Chen
    • 1
  • Yong He
    • 2
  1. 1.Nanjing UniversityNanjingPeople’s Republic of China
  2. 2.Jiangsu Police InstituteNanjingPeople’s Republic of China

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