Skip to main content
SpringerLink
Log in

A Cohesive Subgraph Visualization-Based Approach to Efficiently Discover Large k-Clique Community

  • Research Article – Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Community detection is widely used in many applications such as social network analysis, collaborative filtering and information retrieval. There are many definitions on community of different characteristics and k-Clique community is the one laying its emphasis on the overlapping between communities. We analyze the association between cohesive subgraph visualization (CSV) plot and k-clique community detection. Then, we propose an algorithm named LargeKCliqueCSV to detect large k-clique communities, which reduces search space through CSV plot. At last, we conduct experiments on Stock Market Data datasets. Experimental results show the good scalability of our algorithm comparing with the state-of-art methods Clique Percolation Method and Sequential Clique Percolation algorithm when k grows large.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, J.; Zeng, Z.; Zhou, L.: Clan: An algorithm for mining closed cliques from large dense graph databases. In: Proceedings of the International Conference on Data Engineering, Atlanta, pp. 73–83 (2006)

  2. Asur S., Ucar D., Parthasarathy S.: An ensemble framework for clustering protein–lcprotein interaction networks. Bioinformatics 23, 29–40 (2007)

    Article  Google Scholar 

  3. Madeira S.C., Oliveira A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM TCBB. 1, 24–45 (2004)

    Google Scholar 

  4. Guimerà à R., Amaral L.A.N.: Functional cartography of complex metabolic networks. Nature. 433(7028), 895–900 (2005)

    Article  Google Scholar 

  5. Freeman L.C.: The sociological concept of “group”: An empirical test of two models. Am. J. Sociol. 98(1), 152–166 (1992)

    Article  Google Scholar 

  6. Carrington P.J., Scott J., Wasserman S.: Models and methods in social network analysis. Cambridge University Press, Cambridge (2005)

    Book  Google Scholar 

  7. Flake, G.W.; Lawrence, S.; Giles, C.L.: Efficient identification of web communities. SIGKDD 2000, pp. 150–160. ACM Press, New York (2000)

  8. Palla G., Derényi I., Farkas I., Vicsek T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)

    Article  Google Scholar 

  9. Seidman S.B.: Network structure and minimum degree. Social Netw 5, 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  10. Girvan M., Newman M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA. 99, 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Newman M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E. 69, 066133 (2004)

    Article  Google Scholar 

  12. Flake G.W.R., Tarjan E., Tsioutsiouliklis K.: Graph clustering and minimum cut trees. Internet Math. 1(3), 355–378 (2004)

    MathSciNet  Google Scholar 

  13. Xu, K.K.; Tang, C.J.; Li, C.; et al.: An MDL Approach to Efficiently Discover Communities in Bipartite Network. In: Proceedings of the 15th International Conference on Database Systems for Advanced Applications, DASFAA 2010, pp. 595–611. Tsukuba (2010)

  14. Derényi, I.; Palla, G.; Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94(160202) (2005)

  15. Hui, P.; Yoneki, E.; Chan, S.Y.; et al.: Distributed Community Detection in Delay Tolerant Networks. In: Proceedings of 2nd ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture. ACM press, New York (2007)

  16. Yoneki, E.; Hui, P.; Crowcroft, J.: Visualizing community detection in opportunistic networks. In: Proceedings of the Second ACM Workshop on Challenged Networks, pp. 93–96. ACM press, New York (2007)

  17. Palla G., Barabasi A., Vicsec T. et al.: Quatifying social group evolution. Nature. 446, 664–667 (2007)

    Article  Google Scholar 

  18. Kumpula J.M., Kivela M., Kaski K. et al.: Sequential algorithm for fast clique percolation. Phys. Rev. E. 78, 026109 (2008)

    Article  Google Scholar 

  19. Wan, N.; Parthasarathy, S.; Tan, K.; et al.: CSV: Visualizing and mining cohesive subgraphs. SIGMOD2008, pp. 445–457, Vancouver (2008)

  20. Newman M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  21. Danon, L.; Duch, J.; Diaz-Guilera, A.; Arenas, A.: Comparing community structure identification. J Stat Mech. P09008 (2005)

  22. Fortunato S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  23. Adamcsek B., Palla G., Farkas I.J., Derenyi I., Vicsek T.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics. 22, 1021–1023 (2006)

    Article  Google Scholar 

  24. Baumes, J.; Goldberg, M.; Krishnamoorty, M.; Magdon-Ismail, M.; Preston, N.: Finding communities by clustering a graph into overlapping subgraphs. In: International Conference on Applied Computing (IADIS 2005), pp. 97–104, Algarve (2005)

  25. Baumes, J.; Goldberg, M.; Magdon-Ismail, M.: Efficient identification of overlapping communities. Intelligence and Security Informatics (LNCS 3495), pp. 27–36. Springer, Berlin (2005)

  26. Li, X.; Liu, B.; Yu, P.S.: Discovering overlapping communities of named entities. Knowledge Discovery in Databases: PKDD 2006 (LNCS 4213), pp. 593–600. Springer, Berlin (2006)

  27. Gregory, S.: An algorithm to find overlapping community structure in networks. Knowledge Discovery in Databases: PKDD 2007. (LNCS 4702), pp. 91–102. Springer, Berlin (2007)

  28. McDaid, A.; Hurley, N.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 112–119, Odense (2010)

  29. Brandes U., Erlebach T.: Network Analysis: Methodological Foundations. Springer, Berlin (2005)

    MATH  Google Scholar 

  30. Ng, A.Y.; Jordan, M.I.; Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems 2001, pp. 849–856. IEEE press, Vancouver (2001)

  31. Hu H., Yan X., Huang Y. et al.: Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics. 21(1), 213–221 (2005)

    Article  Google Scholar 

  32. Ankerst, M.; Breunig, M.; Kriegel, H.P.; et al.: OPTICS: Ordering points to identify the clustering structure. SIGMOD1999, pp. 49–60. ACM press, Philadelphia (1999)

  33. Konc J., Janezic D.: An improved branch and bound algorithm for the maximum clique problem. MATCH Commun. Math. Comput. Chem. 58, 569–590 (2007)

    MathSciNet  Google Scholar 

  34. Niskanen, S.; Östergård, P.R.J.: Cliquer User’s Guide, Version 1.0. Tech. Rep. T48. Communications Laboratory, Helsinki University of Technology. Espoo, Finland (2003)

  35. Boginski V. et al.: On structural properties of the market graph. In: Nagurney, A. (eds) Innovations in Financial and Economic Networks, Edward Elgar Publishers, UK (2004)

    Google Scholar 

  36. Wei X.Z., Tang C.J., Xu K.K. et al.: CCDCD: community core mining with dynamic constrains based on graph density. J. Frontiers Comput. Sci. Technol. FCST 3(3), 309–320 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Chengdu University of Information Technology, Chengdu, 610225, People’s Republic of China

    Kaikuo Xu, Jia He, Surong Zou, Hongwei Zhang & Tianyun Yan

  2. Electronic Experiment Center, Chengdu University of Information Technology, Chengdu, 610225, People’s Republic of China

    Xuzhong Wei

Authors
  1. Kaikuo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Surong Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianyun Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuzhong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaikuo Xu.

Additional information

This work was supported by the Science Foundation of Chengdu University of Information Technology under Grand No. KYTZ201108, KYTZ200901, KYTZ200813 and the Key Fund of Education Department of Sichuan Province under Grant No. 09ZA156.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, K., He, J., Zou, S. et al. A Cohesive Subgraph Visualization-Based Approach to Efficiently Discover Large k-Clique Community. Arab J Sci Eng 37, 1959–1968 (2012). https://doi.org/10.1007/s13369-012-0299-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-012-0299-x

Keywords

Search

Navigation