Efficient Topological OLAP on Information Networks

  • Qiang Qu
  • Feida Zhu
  • Xifeng Yan
  • Jiawei Han
  • Philip S. Yu
  • Hongyan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6587)


We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these techniques are weaved. To the best of our knowledge, this is the first work to give a framework study for topological OLAP on information networks. Experimental results demonstrate both the effectiveness and efficiency of our proposed framework.


Short Path Query Processing Information Network Closeness Centrality Data Cube 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Archambault, D., Munzner, T., Auber, D.: TopoLayout: Multilevel graph layout by topological features. IEEE Trans. Vis. Comput. Graph. 13(2), 305–317 (2007)CrossRefGoogle Scholar
  2. 2.
    Beyer, K.S., Ramakrishnan, R.: Bottom-up computation of sparse and iceberg cubes. In: SIGMOD Conference, pp. 359–370 (1999)Google Scholar
  3. 3.
    Boldi, P., Vigna, S.: The WebGraph framework I: Compression techniques. In: WWW, pp. 595–602 (2004)Google Scholar
  4. 4.
    Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1) (2006)Google Scholar
  5. 5.
    Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: Towards online analytical processing on graphs. In: Proc. 2008 Int. Conf. Data Mining (ICDM) (2008)Google Scholar
  6. 6.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C. (eds.): Introduction to Algorithms. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  7. 7.
    Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J.D.: Computing iceberg queries efficiently. In: VLDB, pp. 299–310 (1998)Google Scholar
  8. 8.
    Flake, G., Lawrence, S., Giles, C.L., Coetzee, F.: Self-organization and identification of web communities. IEEE Computer 35, 66–71 (2002)CrossRefGoogle Scholar
  9. 9.
    Gibson, D., Kumar, R., Tomkins, A.: Discovering large dense subgraphs in massive graphs. In: VLDB, pp. 721–732 (2005)Google Scholar
  10. 10.
    Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Disc. 1(1), 29–53 (1997)CrossRefGoogle Scholar
  11. 11.
    Gupta, A., Mumick, I.S. (eds.): Materialized Views: Techniques, Implementations, and Applications. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: A survey. IEEE Trans. Vis. Comput. Graph. 6(1), 24–43 (2000)CrossRefGoogle Scholar
  13. 13.
    Jensen, D., Neville, J.: Data mining in networks. In: Papers of the Symp. Dynamic Social Network Modeling and Analysis. National Academy Press, Washington DC (2002)Google Scholar
  14. 14.
    Jin, R., Xiang, Y., Ruan, N., Wang, H.: Efficiently answering reachability queries on very large directed graphs. In: SIGMOD 2008: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 595–608. ACM, New York (2008)CrossRefGoogle Scholar
  15. 15.
    Kleinberg, J.M., Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: The web as a graph: Measurements, models, and methods. In: Asano, T., Imai, H., Lee, D.T., Nakano, S.-i., Tokuyama, T. (eds.) COCOON 1999. LNCS, vol. 1627, pp. 1–17. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: Densification laws, shrinking diameters and possible explanations. In: Proc. 2005 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2005), Chicago, IL, pp. 177–187 (August 2005)Google Scholar
  17. 17.
    Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: SIGMOD Conference, pp. 419–432 (2008)Google Scholar
  18. 18.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS, pp. 849–856 (2001)Google Scholar
  20. 20.
    Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained association rules. In: SIGMOD Conference, pp. 13–24 (1998)Google Scholar
  21. 21.
    Raghavan, S., Garcia-Molina, H.: Representing web graphs. In: ICDE, pp. 405–416 (2003)Google Scholar
  22. 22.
    Sun, J., Xie, Y., Zhang, H., Faloutsos, C.: Less is more: Sparse graph mining with compact matrix decomposition. Stat. Anal. Data Min. 1(1), 6–22 (2008)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Tian, Y., Hankins, R.A., Patel, J.M.: Efficient aggregation for graph summarization. In: SIGMOD Conference, pp. 567–580 (2008)Google Scholar
  24. 24.
    Wang, N., Parthasarathy, S., Tan, K.-L., Tung, A.K.H.: CSV: visualizing and mining cohesive subgraphs. In: SIGMOD Conference, pp. 445–458 (2008)Google Scholar
  25. 25.
    Wei, F.: Tedi: efficient shortest path query answering on graphs. In: SIGMOD 2010: Proceedings of the 2010 International Conference on Management of Data, pp. 99–110. ACM, New York (2010)Google Scholar
  26. 26.
    Wu, A.Y., Garland, M., Han, J.: Mining scale-free networks using geodesic clustering. In: KDD, pp. 719–724 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Qiang Qu
  • Feida Zhu
  • Xifeng Yan
  • Jiawei Han
  • Philip S. Yu
  • Hongyan Li

There are no affiliations available

Personalised recommendations