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Detecting community structure in networks

The European Physical Journal B volume 38, pages 321–330 (2004)Cite this article

Abstract.

There has been considerable recent interest in algorithms for finding communities in networks--groups of vertices within which connections are dense, but between which connections are sparser. Here we review the progress that has been made towards this end. We begin by describing some traditional methods of community detection, such as spectral bisection, the Kernighan-Lin algorithm and hierarchical clustering based on similarity measures. None of these methods, however, is ideal for the types of real-world network data with which current research is concerned, such as Internet and web data and biological and social networks. We describe a number of more recent algorithms that appear to work well with these data, including algorithms based on edge betweenness scores, on counts of short loops in networks and on voltage differences in resistor networks.

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References

  1. M. Girvan, M.E.J. Newman, Proc. Natl. Acad. Sci. USA 99, 7821 (2002)

    ADS  MathSciNet  Article  Google Scholar 

  2. D. Wilkinson, B.A. Huberman, preprint cond-mat/0210147 (2002)

  3. R. Guimerá, L. Danon, A. Díaz-Guilera, F. Giralt, A. Arenas, Phys. Rev. E 68, 065103 (2003)

    ADS  Article  Google Scholar 

  4. P. Holme, M. Huss, H. Jeong, Bioinformatics 19, 532 (2003)

    Article  Google Scholar 

  5. P. Holme, M. Huss, Proceedings of 3rd Workshop on Computation of Biochemical Pathways and Genetic Networks, edited by R. Gauges, U. Kummer, J. Pahle, U. Rost (Logos, Berlin, 2003), pp. 3-9

  6. J.R. Tyler, D.M. Wilkinson, B.A. Huberman, in Proceedings of the First International Conference on Communities and Technologies, edited by M. Huysman, E. Wenger, V. Wulf (Kluwer, Dordrecht, 2003)

  7. P. Gleiser, L. Danon, preprint cond-mat/0307434 (2003)

  8. M. Boguñá, R. Pastor-Satorras, A. Díaz-Guilera, A. Arenas, preprint cond-mat/0309263 (2003)

  9. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, D. Parisi, preprint cond-mat/0309488 (2003)

  10. F. Wu, B.A. Huberman, preprint cond-mat/0310600 (2003)

  11. D. Gibson, J. Kleinberg, P. Raghavan, in Proceedings of the 9th ACM Conference on Hypertext and Hypermedia (Association of Computing Machinery, New York, 1998)

  12. G.W. Flake, S.R. Lawrence, C.L. Giles, F.M. Coetzee, IEEE Computer 35, 66 (2002)

    Article  Google Scholar 

  13. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Science 298, 824 (2002)

    ADS  Article  Google Scholar 

  14. S. Shen-Orr, R. Milo, S. Mangan, U. Alon, Nature Genetics 31, 64 (2002)

    Article  Google Scholar 

  15. M. Fiedler, Czech. Math. J. 23, 298 (1973)

    MathSciNet  Google Scholar 

  16. A. Pothen, H. Simon, K.-P. Liou, SIAM J. Matrix Anal. Appl. 11, 430 (1990)

    MathSciNet  Article  Google Scholar 

  17. B.W. Kernighan, S. Lin, Bell Sys. Techn. J. 49, 291 (1970)

    Article  Google Scholar 

  18. W.W. Zachary, J. Anthropological Research 33, 452 (1977)

    Article  Google Scholar 

  19. H. Zhou, Phys. Rev. E 67, 061901 (2003)

    ADS  Article  Google Scholar 

  20. G.H. Golub, C.F. Van Loan, Matrix computations (Johns Hopkins University Press, Baltimore, MD, 1989)

  21. J. Scott, Social Network Analysis: A Handbook (Sage, London, 2000), 2nd ed.

  22. C. Bron, J. Kerbosch, Comm. ACM 16, 575 (1973)

    Article  Google Scholar 

  23. R.S. Burt, Social Forces 55, 93 (1976)

    MathSciNet  Article  Google Scholar 

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

  25. R.K. Ahuja, T.L. Magnanti, J.B. Orlin, Network Flows: Theory, Algorithms, and Applications (Prentice Hall, Upper Saddle River, NJ, 1993)

  26. R.E. Tarjan, SIAM J. Comput. 1, 146 (1972)

    MathSciNet  Article  Google Scholar 

  27. J.E. Hopcroft, R.E. Tarjan, SIAM J. Comput. 2, 135 (1973)

    MathSciNet  Article  Google Scholar 

  28. D.R. White, F. Harary, Sociological Methodology 31, 305 (2001)

    Article  Google Scholar 

  29. P.S. Bearman, J. Moody, K. Stovel, preprint, Department of Sociology, Columbia University (2002)

  30. M.J. Fischer, in Complexity of Computer Computations, edited by R.E. Miller, J.W. Thatcher (Plunum Press, New York, 1972), pp. 153-167

  31. R.E. Tarjan, J. ACM 22, 215 (1975)

    MathSciNet  Article  Google Scholar 

  32. L.C. Freeman, Sociometry 40, 35 (1977)

    Article  Google Scholar 

  33. J.M. Anthonisse, Technical Report BN 9/71, Stichting Mathematicsh Centrum, Amsterdam (1971)

  34. M.E.J. Newman, Phys. Rev. E 64, 016132 (2001)

    ADS  Article  Google Scholar 

  35. U. Brandes, J. Math. Soc. 25, 163 (2001)

    Article  Google Scholar 

  36. D. Lusseau, K. Schneider, O.J. Boisseau, P. Haase, E. Slooten, S.M. Dawson, Behavioral Ecology and Sociobiology 54, 396 (2003)

    Article  Google Scholar 

  37. D. Lusseau, Proc. R. Soc. London B (suppl.) 270, S186 (2003)

  38. M.E.J. Newman, M. Girvan, Phys. Rev. E 69, 026113 (2004)

    ADS  Article  Google Scholar 

  39. R. Pastor-Satorras, A. Vázquez, A. Vespignani, Phys. Rev. Lett. 87, 258701 (2001)

    ADS  Article  Google Scholar 

  40. M.E.J. Newman, Phys. Rev. Lett. 89, 208701 (2002)

    ADS  Article  Google Scholar 

  41. D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)

    ADS  Article  Google Scholar 

  42. M.E.J. Newman, J. Park, Phys. Rev. E 68, 036122 (2003)

    ADS  Article  Google Scholar 

  43. M.E.J. Newman, preprint cond-mat/0309508 (2003)

  44. B. Bollobás, Modern Graph Theory (Springer, New York, 1998)

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Authors and Affiliations

  1. Department of Physics and Center for the Study of Complex Systems, University of Michigan, MI 48109-1120, Ann Arbor, USA

    M. E. J. Newman

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  1. M. E. J. Newman
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Corresponding author

Correspondence to M. E. J. Newman.

Additional information

Received: 10 November 2003, Published online: 14 May 2004

PACS:

89.75.Hc Networks and genealogical trees - 87.23.Ge Dynamics of social systems - 89.20.Hh World Wide Web, Internet - 05.10.-a Computational methods in statistical physics and nonlinear dynamics

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Newman, M.E.J. Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004). https://doi.org/10.1140/epjb/e2004-00124-y

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  • DOI: https://doi.org/10.1140/epjb/e2004-00124-y

Keywords

  • Social Network
  • Community Structure
  • Hierarchical Cluster
  • Traditional Method
  • Similarity Measure