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Prototype System Based on Heterogeneous Network

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Heterogeneous Information Network Analysis and Applications

Part of the book series: Data Analytics ((DAANA))

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Abstract

Because of significant advantages of heterogeneous information network, it is widely used to model networked data, and many data mining tasks have been exploited on it. Besides that, many prototype systems, even real systems, have been built based on heterogeneous networks. In these systems, heterogeneous networks are constructed, stored, and operated based on real networked data, and many novel applications are designed based on heterogeneous networks. In this chapter, we introduce two prototype systems for recommendation and further give a brief review on other systems based on heterogeneous networks.

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Notes

  1. 1.

    www.imdb.com/.

  2. 2.

    www.douban.com/.

  3. 3.

    http://aminer.org/.

  4. 4.

    http://pminer.org/home.do?m=home.

References

  1. Balabanovic, M., Shoham, Y.: Content-based collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  2. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)

    Google Scholar 

  3. Danilevsky, M., Wang, C., Tao, F., Nguyen, S., Chen, G., Desai, N., Wang, L., Han, J.: Amethyst: a system for mining and exploring topical hierarchies of heterogeneous data. In: KDD, pp. 1458–1461 (2013)

    Google Scholar 

  4. Han, J.: Mining heterogeneous information networks by exploring the power of links. In: DS, pp. 13–30 (2009)

    Google Scholar 

  5. Jin, X., Lin, C.X., Luo, J., Han, J.: Socialspamguard: a data mining-based spam detection system for social media networks. Proc. Vldb Endow. 4(12), 1458–1461 (2011)

    Google Scholar 

  6. Jin, X., Wang, C., Luo, J., Yu, X., Han, J.: LikeMiner: a system for mining the power of ‘like’ in social media networks. In: KDD, pp. 753–756 (2011)

    Google Scholar 

  7. Lao, N., Cohen, W.: Fast query execution for retrieval models based on path constrained random walks. In: KDD, pp. 881–888 (2010)

    Google Scholar 

  8. Shang, M.S., Lu, L., Zhang, Y.C., Zhou, T.: Empirical analysis of web-based user-object bipartite networks. In: EPL, vol. 90(0120), p. 48006 (2010)

    Google Scholar 

  9. Sharma, A., Cosley, D.: Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In: WWW, pp. 1133–1143 (2013)

    Google Scholar 

  10. 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 

  11. Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. Comput. Sci. 134(12), 87–99 (2015)

    Google Scholar 

  12. 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 

  13. Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. 14(2), 20–28 (2012)

    Article  Google Scholar 

  14. Sun, Y.Z., Han, J.W., Yan, X.F., Yu, P.S., Wu, T.: PathSim: meta path-based top-K similarity search in heterogeneous information networks. In: VLDB, pp. 992–1003 (2011)

    Google Scholar 

  15. Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Moviexplain: a recommender system with explanations. In: RecSys, pp. 317–320 (2009)

    Google Scholar 

  16. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD, pp. 990–998 (2008)

    Google Scholar 

  17. Tang, J., Wang, B., Yang, Y., Hu, P., Zhao, Y., Yan, X., Gao, B., Huang, M., Xu, P., Li, W., Others: PatentMiner: topic-driven patent analysis and mining. In: KDD, pp. 1366–1374 (2012)

    Google Scholar 

  18. Tao, F., Yu, X., Lei, K.H., Brova, G., Cheng, X., Han, J., Kanade, R., Sun, Y., Wang, C., Wang, L., Others: Research-insight: providing insight on research by publication network analysis. In: SIGMOD, pp. 1093–1096 (2013)

    Google Scholar 

  19. Tao, F., Brova, G., Han, J., Ji, H., Wang, C., Norick, B., El-Kishky, A., Liu, J., Ren, X., Sun, Y.: NewsNetExplorer: automatic construction and exploration of news information networks. In: SIGMOD, pp. 1091–1094 (2014)

    Google Scholar 

  20. Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: ICDE Workshop, pp. 801–810 (2007)

    Google Scholar 

  21. Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: IUI, pp. 47–56 (2009)

    Google Scholar 

  22. Wang, B., Ester, M., Bu, J., Cai, D.: Who also likes it? Generating the most persuasive social explanations in recommender systems. In: AAAI, pp. 173–179 (2014)

    Google Scholar 

  23. Yu, X., Sun, Y., Zhao, P., Han, J.: Query-driven discovery of semantically similar substructures in heterogeneous networks. In: KDD, pp. 1500–1503 (2012)

    Google Scholar 

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Chuan Shi

  2. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

Authors
  1. Chuan Shi
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  2. Philip S. Yu
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Correspondence to Chuan Shi .

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Shi, C., Yu, P.S. (2017). Prototype System Based on Heterogeneous Network. In: Heterogeneous Information Network Analysis and Applications. Data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-56212-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-56212-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56211-7

  • Online ISBN: 978-3-319-56212-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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