Skip to main content
SpringerLink
Log in

Self-Organizing Network Evolving Model for Mining Network Community Structure

  • Conference paper
Book cover Advanced Data Mining and Applications (ADMA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4093))

Included in the following conference series:

Abstract

Community structure is an important topological property of network. Being able to discover it can provide invaluable help in exploiting and understanding complex networks. Although many algorithms have been developed to complete this task, they all have advantages and limitations. So the issue of how to detect communities in networks quickly and correctly remains an open challenge. Distinct from the existing works, this paper studies the community structure from the view of network evolution and presents a self-organizing network evolving algorithm for mining communities hidden in complex networks. Compared with the existing algorithm, our approach has three distinct features. First, it has a good classification capability and especially works well with the networks without well-defined community structures. Second, it requires no prior knowledge and is insensitive to the build-in parameters. Finally, it is suitable for not only positive networks but also singed networks containing both positive and negative weights.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Batagelj, V.: Semirings for Social Network Analysis. Journal of Mathematical Sociology 19, 53–68 (1994)

    Article  MATH  Google Scholar 

  2. Strogatz, S.H.: Exploring Complex Networks. Nature 410, 268–276 (2001)

    Article  Google Scholar 

  3. Watts, D.J., Strogatz, S.H.: Collective Dynamics of Small-World Networks. Nature 393, 440–442 (1998)

    Article  Google Scholar 

  4. Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications, London (2000)

    Google Scholar 

  5. Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. J. Anthropological Research 33, 452–473 (1977)

    Google Scholar 

  6. Williams, R.J., Martinez, N.D.: Simple Rules Yield Complex Food Webs. Nature 404, 180–183 (2000)

    Article  Google Scholar 

  7. May, R.M., Lloyd, A.L.: Infection Dynamics on Scale-Free Networks. Physical Rev. E. 64, 066112 (2001)

    Article  Google Scholar 

  8. Jeong, H., Tombor, B., Albert, R., Oltvai, Z., Barabasi, A.: The large-scale organization of metabolic networks. Nature 406, 651–654 (2000)

    Google Scholar 

  9. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On Power-Law Relationships of the Internet Topology. Computer Comm. Rev. 29, 251–262 (1999)

    Article  Google Scholar 

  10. Albert, R., Jeong, H., Barabasi, A.L.: Diameter of the World-Wide Web. Nature 401, 130–131 (1999)

    Article  Google Scholar 

  11. Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random Graphs with Arbitrary Degree Distributions and Their Applications. Physical Rev. E 64, 026118 (2001)

    Article  Google Scholar 

  12. Barabasi, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  13. Newman, M.E.J.: Detecting Community Structure in Networks. European Physical J.B. 38, 321–330 (2004)

    Article  Google Scholar 

  14. Doreian, P., Mrvar, A.: A partitioning approach to structural balance. Social Networks 18, 149–168 (1996)

    Article  Google Scholar 

  15. Sampson, S.: Crisis in a cloister. Unpublished doctoral dissertation, Cornell University (1969)

    Google Scholar 

  16. Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-Organization and Identification of Web Communities. IEEE Computer 35, 66–71 (2002)

    Article  Google Scholar 

  17. Fiedler, M.: Algebraic Connectivity of Graphs. Czechoslovakian Math. J. 23, 298–305 (1973)

    MathSciNet  Google Scholar 

  18. Pothen, A., Simon, H., Liou, K.P.: Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM J. of Matrix Analysis and Application 11, 430–452 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  19. Fiedler, M.: A Property of Eigenvectors of Nonnegative Symmetric Matrices and Its Application to Graph Theory. Czechoslovakian Math. J. 25, 619–637 (1975)

    MathSciNet  Google Scholar 

  20. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Tans. On Pattern analysis and machine Intelligent 22, 888–904 (2000)

    Article  Google Scholar 

  21. Kernighan, B.W., Lin, S.: An Efficient Heuristic Procedure for Partitioning Graphs. Bell System Technical 49, 291–307 (1970)

    Article  MATH  Google Scholar 

  22. Wu, F., Huberman, B.A.: Finding Communities in Linear Time: A Physics Approach. European Physical J. B. 38, 331–338 (2004)

    Article  Google Scholar 

  23. Burt, R.S.: Positions in Networks. Social Forces 55, 93–122 (1976)

    Article  MathSciNet  Google Scholar 

  24. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge Univ. Press, Cambridge (1994)

    Book  Google Scholar 

  25. Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. Proc. Nat’l Academy of Science 9, 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  26. Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as Spectroscopy: Automated Discovery of Community Structure within Organizations. In: Proc. 1st Int’l Conf. Communities and Technologies, Kluwer, Dordrecht (2003)

    Google Scholar 

  27. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and Identifying Communities in Networks. Proc. Nat’l Academy of Science 101, 2658–2663 (2004)

    Article  Google Scholar 

  28. Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. In: Proc. 9th Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 668–677 (1998)

    Google Scholar 

  29. Gibson, D., Kleinberg, J., Raghavan, P.: Inferring Web Communities from Link Topology. In: Proc. 9th ACM Conf. Hypertext and Hypermedia (1998)

    Google Scholar 

  30. Pirolli, P., Pitkow, J., Rao, R.: Silk from a Sow’s Ear: Extracting Usable Structures from the Web. In: Proc. ACM Conf. Human Factors in Computing Systems, CHI, ACM Press, New York (1996)

    Google Scholar 

  31. Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the Web for Emerging Cyber-Communities. In: Proc. 8th Int’l World Wide Web Conf. (1999)

    Google Scholar 

  32. Chakrabarti, S., van der Berg, M., Dom, B.: Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery. In: Proc. 8th Int’l World Wide Web Conf. (1999)

    Google Scholar 

  33. Golub, G.H., Van, L.C.F.: Matrix Computations. Johns Hopkins Univ. Press (1989)

    Google Scholar 

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

    Article  Google Scholar 

  35. Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. Physical Rev. E 69, 026113 (2004)

    Article  Google Scholar 

  36. Lusseau, D.: The Emergent Properties of a Dolphin Social Network. In: Proceedings of the Royal Society of London. Series B, vol. 270, pp. S186–S188 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology & Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, P.R. China

    Bo Yang

Authors
  1. Bo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information Technology and Electronic Engineering, The University of Queensland, Queensland, Australia

    Xue Li

  2. University of Alberta, Canada

    Osmar R. Zaïane

  3. Northwest Polytechnical University, China

    Zhanhuai Li

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, B. (2006). Self-Organizing Network Evolving Model for Mining Network Community Structure. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_45

Download citation

  • DOI: https://doi.org/10.1007/11811305_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37025-3

  • Online ISBN: 978-3-540-37026-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation