Advertisement

SERL: Semantic-Path Biased Representation Learning of Heterogeneous Information Network

  • Haining Tan
  • Weiqiang Tang
  • Xinxin Fan
  • Quanliang Jing
  • Jingping Bi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

The goal of network representation learning is to embed each vertex in a network into a low-dimensional vector space. Existing network representation learning methods can be classified into two categories: homogeneous models that learn the representation of vertexes in a homogeneous information network, and heterogeneous models that learn the representation of vertexes in a heterogeneous information network. In this paper, we study the problem of representation learning of heterogeneous information networks which recently attracts numerous researchers’ attention. Specifically, the existence of multiple types of nodes and links makes this work more challenging. We develop a scalable representation learning models, namely SERL. The SERL method formalizes the way to fuse different semantic paths during the random walk procedure when exploring the neighborhood of corresponding node and then leverages a heterogeneous skip-gram model to perform node embeddings. Extensive experiments show that SERL is able to outperform state-of-the-art learning models in various heterogenous network analysis tasks, such as node classification, similarity search and visualization.

Keywords

Heterogeneous information network Representation learning Semantic path Classification Similarity search 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful comments. This work was supposed by the National Natural Science Foundation of China(Grant No. 61472403, 61303243, 61702470).

References

  1. 1.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, pp. 585–591 (2002)Google Scholar
  2. 2.
    Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900. ACM (2015)Google Scholar
  3. 3.
    Chang, S., Han, W., Tang, J., Qi, G.J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 119–128. ACM (2015)Google Scholar
  4. 4.
    Chen, T., Sun, Y.: Task-guided and path-augmented heterogeneous network embedding for author identification. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 295–304. ACM (2017)Google Scholar
  5. 5.
    Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144. ACM (2017)Google Scholar
  6. 6.
    Fu, T.y., Lee, W.C., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings ACM on Conference on Information and Knowledge Management, pp. 1797–1806. ACM (2017)Google Scholar
  7. 7.
    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
  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.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  10. 10.
    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
  11. 11.
    Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. arXiv preprint arXiv:1711.10730 (2017)
  12. 12.
    Sun, Y., Han, J.: Mining heterogeneous information networks: principles and methodologies. Synth. Lect. Data Mining Knowl. Discov. 3(2), 1–159 (2012)CrossRefGoogle Scholar
  13. 13.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endowment 4(11), 992–1003 (2011)Google Scholar
  14. 14.
    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
  15. 15.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)Google Scholar
  16. 16.
    Wang, C., Song, Y., Li, H., Zhang, M., Han, J.: KnowSim: a document similarity measure on structured heterogeneous information networks. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 1015–1020. IEEE (2015)Google Scholar
  17. 17.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016)Google Scholar
  18. 18.
    Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., Liu, Q.: Shine: Signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 592–600. ACM (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations