Structure, Attribute and Homophily Preserved Social Network Embedding

  • Le Zhang
  • Xiang Li
  • Jiahui Shen
  • Xin WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


Network embedding is to map nodes in a network into low-dimensional vector representations such that the information conveyed by the original network can be effectively captured. We hold that a social network mainly contains three types of information: network structure, node attributes, and their correlation called homophily. All of these information could be potentially helpful in learning an informative network representation. However, most existing network embedding methods only consider one or two types of these information, which are possibly leading to generate unsatisfactory representation. In this paper, we propose a novel algorithm called Structure, Attribute, and Homophily Preserved (SAHP), which jointly exploits the aforementioned three information for learning desirable network representation. And we design a joint optimization framework to embed the three information into a consistent subspace where the interplay between them is captured toward learning optimal network representations. Experiments conducted on three real-world social networks demonstrate that the proposed algorithm SAHP outperforms the state-of-the-art network embedding methods.


Network embedding Network representation learning Social network 



This work is supported by the National Key Research and Development Program of China with Grant No. 2016YFB0800504, and by the National Natural Science Foundation of China with Grant No. U163620068.


  1. 1.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  2. 2.
    Cavallari, S., Zheng, V.W., Cai, H., Chang, K.C.C., Cambria, E.: Learning community embedding with community detection and node embedding on graphs. In: CIKM, pp. 377–386 (2017)Google Scholar
  3. 3.
    Chen, T., Tang, L.A., Sun, Y., Chen, Z., Zhang, K.: Entity embedding-based anomaly detection for heterogeneous categorical events. In: IJCAI, pp. 1396–1403 (2016)Google Scholar
  4. 4.
    Dai, Q., Li, Q., Tang, J., Wang, D.: Adversarial network embedding. In: AAAI, pp. 2167–2174 (2018)Google Scholar
  5. 5.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  7. 7.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)zbMATHGoogle Scholar
  8. 8.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855–864 (2016)Google Scholar
  9. 9.
    Hu, R., Aggarwal, C.C., Ma, S., Huai, J.: An embedding approach to anomaly detection. In: ICDE, pp. 385–396 (2016)Google Scholar
  10. 10.
    Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: SIAM, pp. 633–641 (2017)Google Scholar
  11. 11.
    Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: WSDM, pp. 731–739 (2017)Google Scholar
  12. 12.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR, pp. 1–14 (2017)Google Scholar
  13. 13.
    Li, Y., Sha, C., Huang, X., Zhang, Y.: Community detection in attributed graphs: an embedding approach. In: AAAI, pp. 338–345 (2018)Google Scholar
  14. 14.
    Liang, J., Jacobs, P., Sun, J., Parthasarathy, S.: Semi-supervised embedding in attributed networks with outliers. In: SIAM, pp. 153–161 (2018)Google Scholar
  15. 15.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  17. 17.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NPIS, pp. 3111–3119 (2013)Google Scholar
  18. 18.
    Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: Proceedings of IJCAI, pp. 1895–1901 (2016)Google Scholar
  19. 19.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: SIGKDD, pp. 701–710 (2014)Google Scholar
  20. 20.
    Rahimi, A., Recht, B.: Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems, pp. 1177–1184 (2008)Google Scholar
  21. 21.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)Google Scholar
  22. 22.
    Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks. In: AAAI, pp. 426–433 (2018)Google Scholar
  23. 23.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: SIGKDD, pp. 1225–1234 (2016)Google Scholar
  24. 24.
    Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: AAAI, pp. 203–209 (2017)Google Scholar
  25. 25.
    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
  26. 26.
    Zhang, D., Yin, J., Zhu, X., Zhang, C.: Homophily, structure, and content augmented network representation learning. In: ICDM, pp. 609–618 (2016)Google Scholar
  27. 27.
    Zhang, D., Yin, J., Zhu, X., Zhang, C.: User profile preserving social network embedding. In: IJCAI, pp. 3378–3384 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Le Zhang
    • 1
    • 2
    • 3
  • Xiang Li
    • 1
    • 2
    • 3
  • Jiahui Shen
    • 1
    • 2
    • 3
  • Xin Wang
    • 3
    Email author
  1. 1.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Information SecurityChinese Academy of SciencesBeijingChina
  3. 3.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

Personalised recommendations