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Knowledge and Information Systems

, Volume 49, Issue 2, pp 719–747 | Cite as

Constrained-meta-path-based ranking in heterogeneous information network

  • Chuan Shi
  • Yitong Li
  • Philip S. Yu
  • Bin Wu
Regular Paper

Abstract

Recently, there is a surge of interests on heterogeneous information network analysis, where the network includes different types of objects or links. As a newly emerging network model, heterogeneous information networks have many unique features, e.g., complex structure and rich semantics. Moreover, meta path, the sequence of relations connecting two object types, is widely used to integrate different types of objects and mine the semantics information in this kind of networks. The object ranking is an important and basic function in network analysis, which has been extensively studied in homogeneous networks including the same type of objects and links. However, it is not well exploited in heterogeneous networks until now, since the characteristics of heterogeneous networks introduce new challenges for object ranking. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank method to evaluate the importance of multiple types of objects and meta paths. Since the traditional meta path coarsely embodies path semantics, we propose a constrained meta path to subtly capture the refined semantics through confining constraints on objects. Based on a path-constrained random walk process, HRank can simultaneously determine the importance of objects and constrained meta paths through applying the tensor analysis. Extensive experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.

Keywords

Heterogeneous information network Ranking Random walk Tensor analysis 

Notes

Acknowledgments

This work is supported in part by National Key Basic Research and Department (973) Program of China (No. 2013CB329606), the National Natural Science Foundation of China (No. 71231002, 61375058), the CCF-Tencent Open Fund, the Co-construction Project of Beijing Municipal Commission of Education and US NSF through Grants III-1526499.

References

  1. 1.
    Chakrabarti S (2007) Dynamic personalized pagerank in entity-relation graphs. In: WWW, pp 571–580Google Scholar
  2. 2.
    Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  3. 3.
    Deng H, Lyu MR, King I (2009) A generalized co-hits algorithm and its application to bipartite graphs. In: KDD, pp 239–248Google Scholar
  4. 4.
    Han J (2009) Mining heterogeneous information networks by exploring the power of links. In: Gama J, Costa VS, Jorge AM, Brazdil PB (eds) Discovery Science. Springer, Berlin, Heidelberg, pp 13–30Google Scholar
  5. 5.
    Huang H, Zubiaga A, Ji H, et al (2012) Tweet ranking based on heterogeneous networks. In: Proceedings of the 24th international conference on computational linguistics, COLING 2012, pp 1239–1256Google Scholar
  6. 6.
    Jeh G, Widom J (2002) Simrank: a measure of structural-context similarity. In: KDD, pp 538–543Google Scholar
  7. 7.
    Ji M, Sun Y, Danilevsky M, Han J, Gao J (2010) Graph regularized transductive classification on heterogeneous information networks. In: Balcázar JL, Bonchi F, Gionis A, Sebag M (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 570–586Google Scholar
  8. 8.
    Jin R, Lee VE, Hong H (2011) Axiomatic ranking of network role similarity. In: KDD, pp 922–930Google Scholar
  9. 9.
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. JACM 46(5):604–632Google Scholar
  10. 10.
    Kong X, Yu PS, Ding Y, Wild DJ (2012) Meta path-based collective classification in heterogeneous information networks. In: CIKM, pp 1567–1571Google Scholar
  11. 11.
    Lao N, Cohen W (2010) Fast query execution for retrieval models based on path constrained random walks. In: KDD, pp 881–888Google Scholar
  12. 12.
    Li X, Ng MK, Ye Y (2012) Har: Hub, authority and relevance scores in multi-relational data for query search. In: SDM, pp 141–152Google Scholar
  13. 13.
    Li Y, Shi C, Yu P, Chen Q (2014) Hrank: a path based ranking method in heterogeneous information network. In: Li F, Li G, Hwang Sw, Yao B, Zhang Z (eds) Web-age information management. Lecture Notes in Computer Science, vol 8485. Springer International Publishing, pp 553–565Google Scholar
  14. 14.
    Ng MK, Li X, Ye Y (2011) Multirank: Co-ranking for objects and relations in multi-relational data. In: KDD, pp 1217–1225Google Scholar
  15. 15.
    Nie Z, Zhang Y, Wen J, Ma W (2005) Object-level ranking: bringing order to web objects. In: WWW, pp 422–433Google Scholar
  16. 16.
    Page L, Brin S, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. Technical report, Stanford University Database GroupGoogle Scholar
  17. 17.
    Shi C, Kong X, Yu PS, Xie S, Wu B (2012a) Relevance search in heterogeneous networks. In: EDBT, pp 180–191Google Scholar
  18. 18.
    Shi C, Zhou C, Kong X, Yu PS, Liu G, Wang B (2012b) Heterecom: a semantic-based recommendation system in heterogeneous networks. In: KDD, pp 1552–1555Google Scholar
  19. 19.
    Shi C, Wang R, Li Y, Yu PS, Wu B (2014) Ranking-based clustering on general heterogeneous information networks by network projection. In: CIKM, pp 699–708Google Scholar
  20. 20.
    Shi C, Zhang Z, Luo P, Yu PS, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. In: CIKM, pp 453–462Google Scholar
  21. 21.
    Soulier L, Jabeur LB, Tamine L, Bahsoun W (2013) On ranking relevant entities in heterogeneous networks using a language-based model. J Am Soc Inf Sci Technol 64(3):500–515CrossRefGoogle Scholar
  22. 22.
    Sun Y, Han J, Yan X, Yu P, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. In: VLDB, pp 992–1003Google Scholar
  23. 23.
    Sun Y, Norick B, Han J, Yan X, Yu PS, Yu X (2012) Integrating meta path selection with user-guided object clustering in heterogeneous information networks. In: KDD, pp 1348–1356Google Scholar
  24. 24.
    Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: KDD, pp 990–998Google Scholar
  25. 25.
    Valiant LG (1990) A bridging model for parallel computation. Commun ACM 33(8):103–111Google Scholar
  26. 26.
    Yu X, Sun Y, Norick B, Mao T, Han J (2012) User guided entity similarity search using meta-path selection in heterogeneous information networks. In: CIKM, pp 2025–2029Google Scholar
  27. 27.
    Zhou D, Orshanskiy SA, Zha H, Giles CL (2007) Co-ranking authors and documents in a heterogeneous network. In: ICDM, pp 739–744Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Beijing Key Lab of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Illinois at ChicagoChicagoUSA

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