HRank: A Path Based Ranking Method in Heterogeneous Information Network

  • Yitong Li
  • Chuan Shi
  • Philip S. Yu
  • Qing Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8485)


Recently, there is a surge of interests on heterogeneous information network analysis. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. 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. A constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. 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.


Heterogeneous information network Rank Random walk Tensor analysis 


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  1. 1.
    Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5), 604–632 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Han, J.: Mining Heterogeneous Information Networks by Exploring the Power of Links. Discovery Science (2009)Google Scholar
  3. 3.
    Jeh, G., Widom, J.: Simrank: a Measure of Structural-Context Similarity. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)Google Scholar
  4. 4.
    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, pp. 992–1003 (2011)Google Scholar
  5. 5.
    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
  6. 6.
    Shi, C., Kong, X., Yu, P.S., Xie, S., Wu, B.: Relevance Search in Heterogeneous Networks. In: 15th EDBT, pp. 180–191. ACM (2012)Google Scholar
  7. 7.
    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
  8. 8.
    Nie, Z., Zhang, Y., Wen, J.R., Ma, W.Y.: Object-level Ranking: Bringing Order to Web Objects. In: WWW, pp. 422–433 (2005)Google Scholar
  9. 9.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford University Database Group (1998)Google Scholar
  10. 10.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yitong Li
    • 1
  • Chuan Shi
    • 1
  • Philip S. Yu
    • 2
  • Qing Chen
    • 3
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Illinois at ChicagoUSA
  3. 3.China Mobile Communications CorporationBeijingChina

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