Advertisement

Network Representation Learning Based on Community and Text Features

  • Yu Zhu
  • Zhonglin Ye
  • Haixing Zhao
  • Ke Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

Network representation learning (NRL) aims at building a low-dimensional vector for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Some current NRL methods only focus on learning representations using the network structure. However, vertices in lots of networks may contain community information or text contents, which could be good for relevant evaluation tasks, such as vertex classification, link prediction and so on. Since it has been proved that DeepWalk is actually equivalent to matrix factorization, we propose community and text-enhanced DeepWalk (CTDW) based on the inductive matrix completion algorithm, which incorporates community features and text features of vertices into NRL under the framework of matrix factorization. In experiments, we evaluate the proposed CTDW compared with other state-of-the-art methods on vertex classification. The experimental results demonstrate that CTDW outperforms other baseline methods on three real-world datasets.

Keywords

Network representation learning Community and text features Inductive matrix completion 

Notes

Acknowledgements

The work is supported by the National Natural Science Foundation of China under grant 11661069, grant 61663041 and grant 61763041, the Program for Changjiang Scholars and Innovative Research Team in Universities under grant IRT_15R40, the Key Laboratory of Tibetan Intelligent Information Processing and Machine Translation in Qinghai Normal University, the Research Funds for Chunhui Program of Ministry of Education of China under grant Z2014022, the Natural Science Foundation of Qinghai Province under grant 2013-Z-Y17 and grant 2014-ZJ-721, the Fundamental Research Funds for the Central Universities under grant 2017TS045.

References

  1. 1.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2007)CrossRefGoogle Scholar
  2. 2.
    Tu, C., Liu, Z., Sun, M.: Inferring correspondences from multiple sources for microblog user tags. In: Huang, H., Liu, T., Zhang, H.-P., Tang, J. (eds.) SMP 2014. CCIS, vol. 489, pp. 1–12. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45558-6_1CrossRefGoogle Scholar
  3. 3.
    Yu, H.F., Jain, P., Kar, P., et al.: Large-scale multi-label learning with missing labels. In: Proceedings of ICML, pp. 593–601 (2014)Google Scholar
  4. 4.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  5. 5.
    Mikolov, T., Sutskever, I., Chen, K., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  6. 6.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)Google Scholar
  7. 7.
    Tang, J., Qu, M., Wang, M.Z., et al.: Line: large-scale information network embedding. In: Proceedings of WWW, pp. 1067–1077 (2015)Google Scholar
  8. 8.
    Cao, S.S., Lu, W., Xu, Q.K.: GraRep: learning graph representations with global structural information. In: Conference on Information and Knowledge Management, pp. 891–900 (2015)Google Scholar
  9. 9.
    Wang, D.X., Cui, P., Zhu, W.W.: Structural deep network embedding. In: The ACM SIGKDD International Conference, pp. 1225–1234 (2016)Google Scholar
  10. 10.
    Sun, X.F., Guo, J., Ding, X., et al.: A general framework for content-enhanced network representation learning. arXiv:1610.02906 (2016)
  11. 11.
    Tu, C.C., Wang, H., Zeng, X.K., et al.: Community-enhanced network representation learning for network analysis. arXiv:1611.06645 (2016)
  12. 12.
    Pan, S.R., Wu, J., Zhu, X.Q., et al.: Tri-party deep network representation. In: Proceedings of IJCAI 2016, pp. 1895–1901 (2016)Google Scholar
  13. 13.
    Natarajan, N., Dhillon, I.S.: Inductive matrix completion for predicting gene-disease associations. Bioinformatics 30(12), 60–68 (2014)CrossRefGoogle Scholar
  14. 14.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of ACM, pp. 50–57 (2000)Google Scholar
  15. 15.
    Mei, Q.Z., Cai, D., Zhang, D., et al.: Topic modeling with network regularization. In: Proceedings of WWW, pp. 101–110 (2008)Google Scholar
  16. 16.
    Tu, C.C., Zhang W.C., Liu, Z.Y., et al.: Max-margin DeepWalk: discriminative learning of network representation. In: Proceedings of IJCAI 2016 (2016)Google Scholar
  17. 17.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)Google Scholar
  18. 18.
    Wang, X., Cui, P., Wang, J., et al.: Community preserving network embedding. In: AAAI Conference on Artificial Intelligence 2017 (2017)Google Scholar
  19. 19.
    Yang, C., Liu, Z.Y., Zhao, D.L., et al.: Network representation learning with rich text information. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence 2015 (2015)Google Scholar
  20. 20.
    Krishna, K., Narasimha Murty, M.: Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 29(3), 433–439 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of ComputerQinghai Normal UniversityXiningChina
  2. 2.School of Computer ScienceShaanxi Normal UniversityXi’anChina

Personalised recommendations