Advertisement

When Peculiarity Makes a Difference: Object Characterisation in Heterogeneous Information Networks

  • Wei ChenEmail author
  • Feida Zhu
  • Lei Zhao
  • Xiaofang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)

Abstract

A central task in heterogeneous information networks (HIN) is how to characterise an entity, which underlies a wide range of applications such as similarity search, entity profiling and linkage. Most existing work focus on using the main features common to all. While this approach makes sense in settings where commonality is of primary interest, there are many scenarios as important where uncommon and discriminative features are more useful. To address the problem, a novel model COHIN (Characterize Objects in Heterogeneous Information Networks) is proposed, where each object is characterized as a set of feature paths that contain both main and discriminative features. In addition, we develop an effective pruning strategy to achieve greater query performance. Extensive experiments on real datasets demonstrate that our proposed model can achieve high performance.

Notes

Acknowledgments

This work was partially supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, Media Development Authority (MDA) and the Pinnacle Lab at Singapore Management University, Natural Science Foundation of China (Grant No. 61572335), and Natural Science Foundation of Jiangsu Province, China (Grant No. BK20151223).

References

  1. 1.
    Vosecky, J., Hong, D., Shen, V.Y.: User identification across multiple social networks. In: First International Conference on Networked Digital Technologies NDT2009, pp. 360–365 (2009)Google Scholar
  2. 2.
    Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: ICWSM (2011)Google Scholar
  3. 3.
    Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: SIGKDD, pp. 41–49 (2013)Google Scholar
  4. 4.
    Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: Hydra: large-scale social identity linkage via heterogeneous behavior modeling. In: SIGMOD, pp. 51–62 (2014)Google Scholar
  5. 5.
    Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. JASIST 57(3), 378–393 (2006)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Li, G., Yu, J.X., Feng, J.: Entity matching: how similar is similar. Proc. VLDB Endow. 4(10), 622–633 (2011)CrossRefGoogle Scholar
  7. 7.
    Peled, O., Fire, M., Rokach, L., Elovici, Y.: Entity matching in online social networks. In: Social Computing, pp. 339–344 (2013)Google Scholar
  8. 8.
    Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB (2011)Google Scholar
  9. 9.
    Shi, C., Kong, X., Yu, P.S., Xie, S., Wu, B.: Relevance search in heterogeneous networks. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 180–191 (2012)Google Scholar
  10. 10.
    Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Heidelberg (2014)CrossRefzbMATHGoogle Scholar
  11. 11.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: Rankclus: integrating clustering with ranking for heterogeneous information network analysis. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 565–576 (2009)Google Scholar
  12. 12.
    Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: SIGKDD, pp. 797–806 (2009)Google Scholar
  13. 13.
    Sun, Y., Norick, B., Han, J., Yan, X., Yu, P.S., Yu, X.: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: SIGKDD, pp. 1348–1356 (2012)Google Scholar
  14. 14.
    Gao, M., Lim, E.-P., Lo, D., Zhu, F., Prasetyo, P.K., Zhou, A.: CNL: collective network linkage across heterogeneous social platforms. In: ICDM, pp. 757–762 (2015)Google Scholar
  15. 15.
    Zafarani, R., Liu, H.: Connecting corresponding identities across communities. In: ICWSM (2009)Google Scholar
  16. 16.
    Cohen, W.W., Richman, J.: Learning to match and cluster large high-dimensional data sets for data integration, pp. 475–480 (2002)Google Scholar
  17. 17.
    Tejada, S., Knoblock, C.A., Minton, S.: Learning domain-independent string transformation weights for high accuracy object identification. In: KDD, pp. 350–359 (2002)Google Scholar
  18. 18.
    Yan, X., Cheng, H., Han, J., Yu, P.S.: Mining significant graph patterns by leap search. In: SIGMOD, pp. 433–444 (2008)Google Scholar
  19. 19.
    Thoma, M., Cheng, H., Gretton, A., Han, J., Kriegel, H.-P., Smola, A.J., Song, L., Philip, S.Y., Yan, X., Borgwardt, K.M.: Near-optimal supervised feature selection among frequent subgraphs. In: SDM, pp. 1076–1087 (2009)Google Scholar
  20. 20.
    Zhu, Y., Yu, X.J., Cheng, H., Qin, L.: Graph classification: a diversified discriminative feature selection approach. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 205–214 (2012)Google Scholar
  21. 21.
    Yang, R., Kalnis, P., Tung, A.K.: Similarity evaluation on tree-structured data. In: SIGMOD, pp. 754–765 (2005)Google Scholar
  22. 22.
    Li, G., Liu, X., Feng, J.-H., Zhou, L.: Efficient similarity search for tree-structured data. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 131–149. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wei Chen
    • 1
    • 2
    Email author
  • Feida Zhu
    • 1
  • Lei Zhao
    • 2
  • Xiaofang Zhou
    • 2
    • 3
  1. 1.School of Information SystemsSingapore Management UniversitySingaporeSingapore
  2. 2.School of Computer Science and TechnologySoochow UniversityJiangsuChina
  3. 3.School of ITEEThe University of QueenslandBrisbaneAustralia

Personalised recommendations