Advertisement

Detecting Community Structure by Network Vectorization

  • Wei Ren
  • Guiying Yan
  • Guohui Lin
  • Caifeng Du
  • Xiaofeng Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5092)

Abstract

With the growing number of available social and biological networks, the problem of detecting network community structure is becoming more and more important which acts as the first step to analyze these data. In this paper, we transform network data so that each node is represented by a vector, our method can handle directed and weighted networks. it also can detect networks which contain communities with different sizes and degree sequences. This paper reveals that network community can be formulated as a cluster problem.

Keywords

Community Structure Adjacent Matrix Community Detection Node Pair Latent Semantic Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barabási, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Alves, N.A.: Unveiling Community Structures in Weighted Networks. Phys. Rev. E 76, 036101 (2007)Google Scholar
  3. 3.
    Berry, M.W.: Large-Scale Sparse Singular Value Computations. The International Journal of Supercomputer Applications 6(1), 13–49 (1992)MathSciNetGoogle Scholar
  4. 4.
    Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing Community Structure Identification (2005)Google Scholar
  5. 5.
    Deerwester, S., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by Latent Semantic Analysis. Journal of the Society for Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  6. 6.
    Duch, J., Arenas, A.: Community Detection in Complex Network Using Extremal Optimization. Physical Review E 72, 027104 (2005)Google Scholar
  7. 7.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  8. 8.
    Fortunato, S., Barthlemy, M.: Resolution Limit in Community Detection. Proc. Natl. Acad. Sci. USA 104(1), 36–41 (2007)CrossRefGoogle Scholar
  9. 9.
    Freeman, L.C.: The Sociological Concept of “Group”: An Empirical Test of Two Models. American Journal of Sociology 98, 152–166 (1992)CrossRefGoogle Scholar
  10. 10.
    Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. Proc. Natl. Acad. Sci. USA 99(2), 7821–7826 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  11. 11.
    Leicht, E.A., Newman, M.E.J.: Community Structure in Directed Networks. Phys. Rev. Lett. 100, 118703 (2008)CrossRefGoogle Scholar
  12. 12.
    Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402, 6761 (1999)CrossRefGoogle Scholar
  13. 13.
    Luo, F., Yang, Y., Chen, C.F., Chang, R., Zhou, J., Scheuermann, R.H.: Modular Organization of Protein Interaction Networks. Bioinformatics 23(2), 207–214 (2007)CrossRefGoogle Scholar
  14. 14.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The Bottlenose Dolphin Community of Doubtful Sound Features a Large Proportion of Long-Lasting Associations. Behavioral Ecology and Sociobiology 54, 396–405 (2003)CrossRefGoogle Scholar
  15. 15.
    MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  16. 16.
    Newman, M.E.J.: Finding Community Structure in Networks Using the Eigenvectors of Matrices. Phys. Rev. E 74, 036104 (2006)Google Scholar
  17. 17.
    Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. Phys. Rev. E 69(2), 026113 (2004)Google Scholar
  18. 18.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and Identifying Communities in Networks. Proc. Natl. Acad. Sci. USA 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  19. 19.
    Rosvall, M., Bergstrom, C.T.: An Information-Theoretic Framework for Resolving Community Structure in Complex Networks. Proc. Natl. Acad. Sci. USA 104(18), 7327–7331 (2007)CrossRefGoogle Scholar
  20. 20.
    Watts, D.S.: Collective Dynamics of “Small-World” Networks. Nature 4 393(6684), 409–410 (1998)Google Scholar
  21. 21.
    White, S., Smyth, P.: A Spectral Clustering Approach to Finding Communities in Graphs. In: SIAM International Conference on Data Mining (2005)Google Scholar
  22. 22.
    Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar
  23. 23.
    Zhou, H.: Network Landscape from a Brownian Particle’s Perspective. Phys. Rev. E 67, 041908 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wei Ren
    • 1
  • Guiying Yan
    • 1
  • Guohui Lin
    • 2
  • Caifeng Du
    • 3
  • Xiaofeng Han
    • 4
  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of Science 
  2. 2.Department of Computing ScienceUniversity of Alberta 
  3. 3.College of Mathematics and Computational ScienceChina University of Petroleum 
  4. 4.College of ScienceShandong University of Science and Technology 

Personalised recommendations