Skip to main content
SpringerLink
Log in

Knowledge-Guided Maximal Clique Enumeration

  • Conference paper
  • First Online:
Advanced Data Mining and Applications (ADMA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

Included in the following conference series:

  • 2407 Accesses

Abstract

Maximal clique enumeration is a long-standing problem in graph mining and knowledge discovery. Numerous classic algorithms exist for solving this problem. However, these algorithms focus on enumerating all maximal cliques, which may be computationally impractical and much of the output may be irrelevant to the user. To address this issue, we introduce the problem of knowledge-biased clique enumeration, a query-driven formulation that reduces output space, computation time, and memory usage. Moreover, we introduce a dynamic state space indexing strategy for efficiently processing multiple queries over the same graph. This strategy reduces redundant computations by dynamically indexing the constituent state space generated with each query. Experimental results over real-world networks demonstrate this strategy’s effectiveness at reducing the cumulative query-response time. Although developed in the context of maximal cliques, our techniques could possibly be generalized to other constraint-based graph enumeration tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Noaa earth system research laboratory. http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.html

  2. Abello, J., Resende, M.G.C., Sudarsky, S.: Massive Quasi-Clique detection. In: Rajsbaum, S. (ed.) LATIN 2002. LNCS, vol. 2286, pp. 598–612. Springer, Heidelberg (2002). doi:10.1007/3-540-45995-2_51

    Chapter  Google Scholar 

  3. Angles, R.: A comparison of current graph database models. In: Workshops Proceedings of the IEEE 28th International Conference on Data Engineering, ICDE, pp. 171–177 (2012)

    Google Scholar 

  4. Bast, H., Funke, S., Matijevic, D., Sanders, P., Schultes, D.: In transit to constant time shortest-path queries in road networks. In: Proceedings of the Nine Workshop on Algorithm Engineering and Experiments, ALENEX (2007)

    Google Scholar 

  5. Bron, C., Kerbosch, J.: Finding all cliques of an undirected graph (algorithm 457). Commun. ACM 16(9), 575–576 (1973)

    Article  MATH  Google Scholar 

  6. Creamer, G., Rowe, R., Hershkop, S., Stolfo, S.J.: Segmentation and automated social hierarchy detection through email network analysis. In: Advances in Web Mining and Web Usage Analysis, 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, pp. 40–58 (2007)

    Google Scholar 

  7. Cui, W., Xiao, Y., Wang, H., Lu, Y., Wang, W.: Online search of overlapping communities. In: Proceedings of the International Conference on Management of Data, SIGMOD 2013, pp. 277–288 (2013)

    Google Scholar 

  8. Cui, W., Xiao, Y., Wang, H., Wang, W.: Local search of communities in large graphs. In: International Conference on Management of Data, SIGMOD, pp. 991–1002 (2014)

    Google Scholar 

  9. Davis, A.P., Murphy, C.G., Johnson, R., Lay, J.M., Lennon-Hopkins, K., Saraceni-Richards, C.A., Sciaky, D., King, B.L., Rosenstein, M.C., Wiegers, T.C., Mattingly, C.J.: The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 41(D1), D1104–D1114 (2013)

    Article  Google Scholar 

  10. Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., Jensen, L.J.: STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41(D1), D808–D815 (2013)

    Article  Google Scholar 

  11. Goldenberg, S.B., Shapiro, L.J.: Physical mechanisms for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J. Clim. 9(6), 1169–1187 (1996)

    Article  Google Scholar 

  12. Iordanov, B.: HyperGraphDB: a generalized graph database. In: Web-Age Information Management, pp. 25–36 (2010)

    Google Scholar 

  13. Jia, L., Ye, J., Haiyan, L.V., Wang, W., Zhou, C., Zhang, X., Xu, J., Wang, L., Jia, J.: Genetic association between polymorphisms of pen2 gene and late onset Alzheimer’s disease in the north chinese population. Brain Res. 1141, 10–14 (2007)

    Article  Google Scholar 

  14. Klotzbach, P.J.: On the Madden-Julian oscillation-Atlantic hurricane relationship. J. Clim. 23(2), 282–293 (2010)

    Article  Google Scholar 

  15. Modani, N., Dey, K.: Large maximal cliques enumeration in large sparse graphs. In: Proceedings of the 15th International Conference on Management of Data (2009)

    Google Scholar 

  16. Moon, J.W., Moser, L.: On cliques in graphs. Israel J. Math. 3(1), 23–28 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  17. Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining, KDD. pp. 939–948 (2010)

    Google Scholar 

  18. Steinhaeuser, K., Chawla, N.V., Ganguly, A.R.: Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat. Anal. Data Min. 4(5), 497–511 (2011)

    Article  MathSciNet  Google Scholar 

  19. Tsourakakis, C.E., Bonchi, F., Gionis, A., Gullo, F., Tsiarli, M.A.: Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees. In: The 19th International Conference on Knowledge Discovery and Data Mining, KDD, pp. 104–112 (2013)

    Google Scholar 

  20. Vicknair, C., Macias, M., Zhao, Z., Nan, X., Chen, Y., Wilkins, D.: A comparison of a graph database and a relational database: a data provenance perspective. In: Proceedings of the 48th Annual Southeast Regional Conference, p. 42 (2010)

    Google Scholar 

  21. Vimont, D.J., Kossin, J.P.: The Atlantic meridional mode and hurricane activity. Geophysical Research Letters 34(7) (2007)

    Google Scholar 

  22. Wei, F.: TEDI: efficient shortest path query answering on graphs. In: Graph Data Management: Techniques and Applications., pp. 214–238 (2011)

    Google Scholar 

  23. Wilkinson, K., Sayers, C., Kuno, H.A., Reynolds, D.: Efficient RDF storage and retrieval in Jena2. In: Proceedings of The First International Workshop on Semantic Web and Databases, SWDB, pp. 131–150 (2003)

    Google Scholar 

  24. Xiao, Y., Wu, W., Pei, J., Wang, W., He, Z.: Efficiently indexing shortest paths by exploiting symmetry in graphs. In: 12th International Conference on Extending Database Technology, EDBT, pp. 493–504 (2009)

    Google Scholar 

  25. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: 12th International Conference on Data Mining, ICDM, pp. 745–754 (2012)

    Google Scholar 

  26. Zhang, B., Park, B.H., Karpinets, T., Samatova, N.F.: From pull-down data to protein interaction networks and complexes with biological relevance. Bioinformatics 24(7), 979–986 (2008)

    Article  Google Scholar 

  27. Zhu, F., Yan, X., Han, J., Yu, P.S.: gPrune: a constraint pushing framework for graph pattern mining. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 388–400. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71701-0_38

    Chapter  Google Scholar 

Download references

Acknowledgments

The authors would like to thank David A. Boyuka II and Sriram Lakshminarasimhan for their initial discussions and feedback with this work. This material is based upon work supported in part by the Laboratory for Analytic Sciences and the U.S. DOE Office of Science ASCR. Any opinions or findings expressed in this material are those of the author(s) and do not necessarily reflect the views of any agent or entity of the US government.

Author information

Authors and Affiliations

  1. North Carolina State University, Raleigh, North Carolina, USA

    Steve Harenberg, Ramona G. Seay, Gonzalo A. Bello, Rada Y. Chirkova & Nagiza F. Samatova

  2. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Steve Harenberg, Ramona G. Seay, Gonzalo A. Bello & Nagiza F. Samatova

  3. Duke University, Durham, North Carolina, USA

    P. Murali Doraiswamy

Authors
  1. Steve Harenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramona G. Seay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gonzalo A. Bello
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rada Y. Chirkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Murali Doraiswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nagiza F. Samatova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nagiza F. Samatova .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Harenberg, S., Seay, R.G., Bello, G.A., Chirkova, R.Y., Doraiswamy, P.M., Samatova, N.F. (2016). Knowledge-Guided Maximal Clique Enumeration. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49586-6_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation