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Introduction

  • Chuan ShiEmail author
  • Philip S. Yu
Chapter
Part of the Data Analytics book series (DAANA)

Abstract

In this chapter, we introduce some basic concepts and definitions in heterogeneous information network and compare the heterogeneous information network with other related concepts. Then, we give some popular examples in this field. In the end, we analyze the reason why mining heterogeneous information network is a new paradigm.

Keywords

Information Network Resource Description Framework Heterogeneous Network Object Type Anchor Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chakrabarti, S., et al.: Mining the Web: Analysis of Hypertext and Semi Structured Data. Morgan Kaufmann, San Francisco (2002)Google Scholar
  2. 2.
    Cook, D.J., Holder, L.B.: Graph-based data mining. IEEE Intell. Syst. 15(2), 32–41 (2000)CrossRefGoogle Scholar
  3. 3.
    Feldman, R.: Link analysis: current state of the art. In: Tutorial at the KDD-2 (2002)Google Scholar
  4. 4.
    Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. 7(2), 3–12 (2005)CrossRefGoogle Scholar
  5. 5.
    Jamali, M., Lakshmanan, L.: HeteroMF: recommendation in heterogeneous information networks using context dependent factor models. In: WWW, pp. 643–654 (2013)Google Scholar
  6. 6.
    Jensen, D., Goldberg, H.: AAAI Fall Symposium on AI and Link Analysis. AAAI Press (1998)Google Scholar
  7. 7.
    Kim, J., Wilhelm, T.: What is a complex graph? Phys. A Stat. Mech. Appl. 387(11), 2637–2652 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kong, X., Cao, B., Yu, P.S.: Multi-label classification by mining label and instance correlations from heterogeneous information networks. In: KDD, pp. 614–622 (2013)Google Scholar
  9. 9.
    Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: CIKM, pp. 179–188 (2013)Google Scholar
  10. 10.
    Kong, X., Yu, P.S., Ding, Y., Wild, D.J.: Meta path-based collective classification in heterogeneous information networks. In: CIKM, pp. 1567–1571 (2012)Google Scholar
  11. 11.
    Konstas, I., Stathopoulo, V., Jose, J.M.: On social networks and collaborative recommendation. In: SIGIR, pp. 195–202 (2009)Google Scholar
  12. 12.
    Lewis, T.G.: Network Science: Theory and Applications. Wiley, New York (2011)Google Scholar
  13. 13.
    Li, Y., Shi, C., Yu, P.S., Chen, Q.: HRank: a path based ranking method in heterogeneous information network. In: WAIM, pp. 553–565 (2014)Google Scholar
  14. 14.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM, pp. 556–559 (2003)Google Scholar
  15. 15.
    Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: SDM, pp. 252–260 (2013)Google Scholar
  16. 16.
    Long, B., Zhang, Z.M., Yu, P.S.: Co-clustering by block value decomposition. In: KDD, pp. 635–640 (2005)Google Scholar
  17. 17.
    Long, B., Wu, X., Zhang, Z., Yu, P.S.: Unsupervised learning on k-partite graphs. In: KDD, pp. 317–326 (2006)Google Scholar
  18. 18.
    Long, B., Zhang, Z., Wu, X., Yu, P.S.: Spectral clustering for multi-type relational data. In: ICML, pp. 585–592 (2006)Google Scholar
  19. 19.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Otte, E., Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28(6), 441–453 (2002)CrossRefGoogle Scholar
  21. 21.
    Özsu, M.T.: A survey of RDF data management systems. Front. Comput. Sci. 10(3), 418–432 (2016)CrossRefGoogle Scholar
  22. 22.
    Shi, C., Kong, X., Yu, P.S., Xie, S., Wu, B.: Relevance search in heterogeneous networks. In: International Conference on Extending Database Technology, pp. 180–191 (2012)Google Scholar
  23. 23.
    Shi, C., Zhou, C., Kong, X., Yu, P.S., Liu, G., Wang, B.: HeteRecom: a semantic-based recommendation system in heterogeneous networks. In: KDD, pp. 1552–1555 (2012)Google Scholar
  24. 24.
    Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: The ACM International, pp. 453–462 (2015)Google Scholar
  25. 25.
    Singhal, A.: Introducing the Knowledge Graph: things, not strings. In: Official Google Blog (2012)Google Scholar
  26. 26.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: WWW, pp. 697–706 (2007)Google Scholar
  27. 27.
    Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. 14(2), 20–28 (2012)CrossRefGoogle Scholar
  28. 28.
    Sun, Y., Yu, Y., Han, J.: Ranking-based clustering of heterogeneous information networks with star network schema. In: KDD, pp. 797–806 (2009)Google Scholar
  29. 29.
    Sun, Y., Han, J., Yan, X., Yu, P., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, pp. 992–1003 (2011)Google Scholar
  30. 30.
    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: KDD, pp. 1348–1356 (2012)Google Scholar
  31. 31.
    Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. In: KDD, pp. 677–685 (2008)Google Scholar
  32. 32.
    Tang, J., Gao, H., Hu, X., Liu, H.: Exploiting homophily effect for trust prediction. In: WSDM, pp. 53–62 (2013)Google Scholar
  33. 33.
    Wang, R., Shi, C., Yu, P.S., Wu, B.: Integrating clustering and ranking on hybrid heterogeneous information network. In: PAKDD, pp. 583–594 (2013)Google Scholar
  34. 34.
    Wasserman, S.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  35. 35.
    Wu, X., Zhu, X., Wu, G., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)CrossRefGoogle Scholar
  36. 36.
    Yang, Y., Chawla, N.V., Sun, Y., Han, J.: Predicting links in multi-relational and heterogeneous networks. In: ICDM, pp. 755–764 (2012)Google Scholar
  37. 37.
    Yu, X., Ren, X., Sun, Y., Sturt, B., Khandelwal, U., Gu, Q., Norick, B., Han, J.: Recommendation in heterogeneous information networks with implicit user feedback. In: RecSys, pp. 347–350 (2013)Google Scholar
  38. 38.
    Zhang, J., Yu, P.S.: Integrated anchor and social link predictions across social networks. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pp. 2125–2131. AAAI Press (2015)Google Scholar
  39. 39.
    Zhong, E., Fan, W., Wang, J., Xiao, L., Li, Y.: ComSoc: adaptive transfer of user behaviors over composite social network. In: KDD, pp. 696–704 (2012)Google Scholar
  40. 40.
    Zhong, E., Fan, W., Zhu, Y., Yang, Q.: Modeling the dynamics of composite social networks. In: KDD, pp. 937–945 (2013)Google Scholar
  41. 41.
    Zhuang, H., Zhang, J., Brova, G., Tang, J., Cam, H., Yan, X., Han, J.: Mining query-based subnetwork outliers in heterogeneous information networks. In: ICDM, pp. 1127–1132 (2014)Google Scholar
  42. 42.
    Zou, L., Özsu, M.T., Chen, L., Shen, X., Huang, R., Zhao, D.: gStore: a graph-based SPARQL query engine. VLDB J. 23(4), 565–590 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Illinois at ChicagoChicagoUSA

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