Integrating Clustering and Ranking on Hybrid Heterogeneous Information Network

  • Ran Wang
  • Chuan Shi
  • Philip S. Yu
  • Bin Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7818)


Recently, ranking-based clustering on heterogeneous information network has emerged, which shows its advantages on the mutual promotion of clustering and ranking. However, these algorithms are restricted to information network only containing heterogeneous relations. In many applications, networked data are more complex and they can be represented as a hybrid network which simultaneously includes heterogeneous and homogeneous relations. It is more promising to promote clustering and ranking performance by combining the heterogeneous and homogeneous relations. This paper studied the ranking-based clustering on this kind of hybrid network and proposed the ComClus algorithm. ComClus applies star schema with self loop to organize the hybrid network and uses a probability model to represent the generative probability of objects. Experiments show that ComClus can achieve more accurate clustering results and do more reasonable ranking with quick and steady convergence.


Clustering Ranking Heterogeneous Information Network Probability Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shen, H., Cheng, X.: Spectral Methods for the Detection of Network Community Structure: a Comparative Analysis. J. Stat. Mech., P10020 (2010)Google Scholar
  2. 2.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: Rankclus: Integrating Clustering with Ranking for Heterogeneous Information Network Analysis. In: EDBT, pp. 565–576 (2009)Google Scholar
  3. 3.
    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
  4. 4.
    Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. In: CVPR, pp. 731–737 (1997)Google Scholar
  5. 5.
    Jacobs, R.A., Jordan, M.I., Nowlan, S., Hinton, G.E.: Adaptive Mixtures of Local Experts. Neural Computation 3, 79–87 (1991)CrossRefGoogle Scholar
  6. 6.
    Zhou, D., Orshanskiy, S., Zha, H., Giles, C.: Co-ranking Authors and Documents in a Heterogeneous Network. In: ICDM, pp. 739–744 (2007)Google Scholar
  7. 7.
    Liu, X., Murata, T.: Detecting Communities in K-partite K-uniform (Hyper) Networks. JCST 26(5), 778–791 (2011)Google Scholar
  8. 8.
    Zhang, M.L., Zhang, K.: Multi-label Learning by Exploiting Label Dependency. In: KDD, pp. 999–1008 (2010)Google Scholar
  9. 9.
    Long, B., Wu, X., Zhang, Z.M., Yu, P.S.: Unsupervised Learning on K-partite Graphs. In: KDD, pp. 317–326 (2006)Google Scholar
  10. 10.
    Michael, K.N., Li, X., Ye, Y.: MultiRank: Co-ranking for Objects and Relations in Multi-relational Data. In: KDD, pp. 1217–1225 (2011)Google Scholar
  11. 11.
    Ailon, N., Charikar, M., Newman, A.: Aggregating Inconsistent Information: Ranking and Clustering. J. ACM 55(5) (2008)Google Scholar
  12. 12.
    Brin, S., Page, L.: The Anatomy of a Large-scale Hyper Textual Web Search Engine. Comput. Netw. ISDN Syst. 30(1-7), 107–117 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ran Wang
    • 1
  • Chuan Shi
    • 1
  • Philip S. Yu
    • 2
    • 3
  • Bin Wu
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Illinois at ChicagoUSA
  3. 3.King Abdulaziz University JeddahSaudi Arabia

Personalised recommendations