MultNet: An Efficient Network Representation Learning for Large-Scale Social Relation Extraction

  • Jun Yuan
  • Neng Gao
  • Lei Wang
  • Zeyi Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)


Network representation learning (NRL), which has become an focus of current research, learns low-dimensional vertex representations to capture network information. However, conventional NRL models either largely neglect the rich semantic information on edges and fail to extract good features of relations, or employ complex models that have rather high space and time complexities. In this work, we present an efficient NRL model, MultNet, for Social Relation Extraction (SRE) task, which evaluates the ability of NRL models on modeling the relationships between vertices. We conduct extensive experiments on several public data sets and experiments on SRE indicate that MultNet outperforms other baseline models significantly.


Network representation learning Embedding Social Relation Extraction 



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


  1. 1.
    Bordes, A., Usunier, N., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)Google Scholar
  2. 2.
    Zeng, X., Liu, Z., Tu, C., Wang, H., Sun, M.: Community-enhanced network representation learning for network analysis. arXiv preprint arXiv:1611.06645 (2016)
  3. 3.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS (2010)Google Scholar
  4. 4.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: SIGKDD, pp. 855–864 (2016)Google Scholar
  5. 5.
    Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: ACL, pp. 1004–1013 (2016)Google Scholar
  6. 6.
    Lindamood, J., Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Inferring private information using social network data. In: WWW, pp. 1145–1146 (2013)Google Scholar
  7. 7.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)Google Scholar
  8. 8.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: SIGKDD, pp. 701–710 (2014)Google Scholar
  9. 9.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  10. 10.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys, pp. 259–266 (2008)Google Scholar
  11. 11.
    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
  12. 12.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: SIGKDD, pp. 990–998 (2008)Google Scholar
  13. 13.
    Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  14. 14.
    Tu, C., Liu, H., Liu, Z., Sun, M.: CANE: context-aware network embedding for relation modeling. In: ACL, pp. 1722–1731 (2017)Google Scholar
  15. 15.
    Tu, C., Zhang, W., Liu, Z., Sun, M.: Max-Margin DeepWalk: discriminative learning of network representation. In: IJCAI, pp. 3889–3895 (2016)Google Scholar
  16. 16.
    Tu, C., Zhang, Z., Liu, Z., Sun, M.: TransNet: translation-based network representation learning for social relation extraction. In: International Joint Conference on Artificial Intelligence, pp. 2864–2870 (2017)Google Scholar
  17. 17.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: SIGKDD, pp. 1225–1234 (2016)Google Scholar
  18. 18.
    Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: AAAI (2017)Google Scholar
  19. 19.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2008)Google Scholar
  20. 20.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40 (2007)CrossRefGoogle Scholar
  21. 21.
    Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (2015)Google Scholar
  22. 22.
    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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Data Assurance and Communications Security CenterChinese Academy of SciencesBeijingChina
  3. 3.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

Personalised recommendations