Skip to main content
Log in

An Algorithm for Detecting Communities in Social Networks

  • Published:
Journal of Mathematical Sciences Aims and scope Submit manuscript

Abstract

In this paper, we propose an algorithm to find subgraphs with given properties in large social networks. A computational experiment that confirms the effectiveness of the proposed algorithm is presented.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. T. V. Batura, “Methods of analysis of computer social networks,” Vestn. NGU, Ser. Inform. Technol., 10, No. 4, 13–28 (2012).

    Google Scholar 

  2. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “The Louvain method for community detection in large networks,” J. Stat. Mech. Theory Exp., 108–121 (2008).

  3. A. N. Churakov, “Social network analysis,” SocIs, 1, 109–121 (2001).

    Google Scholar 

  4. A. Clauset, M. E. Newman, and C. Moore, “Finding community structure in very large networks,” Phys. Rev., E 70, 066111 (2004).

    Google Scholar 

  5. S. Fortunato, “Community detection in graphs,” Phys. Rep., 486, 75–174 (2010).

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  7. R. Guimera, M. Sales-Pardo, and L. A. N. Amaral, “Modularity from fluctuations in random graphs and complex networks,” Phys. Rev., E 70, 025101 (2004).

    Google Scholar 

  8. R. Lambiotte and M. Rosvall, “Ranking and clustering of nodes in networks with smart teleportation,” Phys. Rev., E 85, 1103–1012 (2012).

    Google Scholar 

  9. A. Lancichinetti and S. Fortunato, “Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities,” Phys. Rev., E 80, 016118 (2009).

    Google Scholar 

  10. A. Lancichinetti and S. Fortunato, “Community detection algorithms: a comparative analysis,” Phys. Rev., E 80, 056117 (2009).

    Google Scholar 

  11. A. Lancichinetti, S. Fortunato, and F. Radicchi, “Benchmark graphs for testing community detection algorithms,” Phys. Rev., E 78, 046110 (2008).

    Google Scholar 

  12. L. Lovasz, “Random walks on graphs: a survey,” in: D. Miklós, V. T. Sós, and T. Sz\( \overset{^{{\prime\prime} }}{\mathrm{o}} \)nyi, eds., Combinatorics, Paul Erd \( \overset{^{{\prime\prime} }}{o} \) s is Eighty, Bolyai Soc. Math. Stud., Vol. 2, Budapest (1996), pp. 1–46.

  13. C. P. Massen and J. P. K. Doye, “Identifying communities within energy landscapes,” Phys. Rev., E 71, 046101 (2005).

    MathSciNet  Google Scholar 

  14. M. E. Newman, “Fast algorithm for detecting community structure in networks,” Phys. Rev., E 69, 066133 (2004).

    Google Scholar 

  15. M. E. Newman, “Modularity and community structure in networks,” Proc. Natl. Acad. Sci. USA, 103, 8577–8582 (2006).

    Article  Google Scholar 

  16. M. E. Newman, Networks: An Introduction, Oxford Univ. Press, Oxford (2010).

    Book  Google Scholar 

  17. M. E. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Phys. Rev., E 69, 026113 (2004).

    Google Scholar 

  18. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, “Defining and identifying communities in networks,” Proc. Natl. Acad. Sci. USA, 101, 2658–2663 (2004).

    Article  Google Scholar 

  19. M. Rosvall, D. Axelsson, and C. T. Bergstrom, “The map equation,” Eur. Phys. J. Special Topics, 178, No. 1, 13–23 (2009).

    Article  Google Scholar 

  20. M. Rosvall and C. T. Bergstrom, “An information-theoretic framework for resolving community structure in complex networks,” Proc. Natl. Acad. Sci. USA, 104, No. 18, 7327–7331 (2007).

    Article  Google Scholar 

  21. M. Rosvall and C. T. Bergstrom, “Maps of information flow reveal community structure in complex networks,” Proc. Natl. Acad. Sci. USA, 105, No. 4, 1118–1123 (2008).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. A. Chepovskiy.

Additional information

Translated from Fundamentalnaya i Prikladnaya Matematika, Vol. 19, No. 1, pp. 21–32, 2014.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolomeychenko, M.I., Chepovskiy, A.A. & Chepovskiy, A.M. An Algorithm for Detecting Communities in Social Networks. J Math Sci 211, 310–318 (2015). https://doi.org/10.1007/s10958-015-2607-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10958-015-2607-y

Keywords

Navigation