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Detecting Communities in Massive Networks Efficiently with Flexible Resolution

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The Influence of Technology on Social Network Analysis and Mining

Part of the book series: Lecture Notes in Social Networks ((LNSN,volume 6))

Abstract

Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this chapter, we propose an iterative heuristic algorithm (called MMO algorithm) to extract the community structure in large networks based on local multi-resolution modularity optimization whose time complexity is near linear and space complexity is linear. The effectiveness of MMO algorithm is demonstrated by extensive experiments on lots of computer generated graphs and publically available real-world graphs. We also extend MMO algorithm to extract communities in distributed environment and use it to explore a massive call graph on a normal PC. The results show that MMO algorithm is very efficient, and it may enhance our ability to explore massive networks in real time.

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Notes

  1. 1.

    http://snap.stanford.edu

  2. 2.

    http://www-personal.umich.edu/~mejn/netdata/

  3. 3.

    http://deim.urv.cat/~aarenas/data/welcome.htm

  4. 4.

    http://snap.stanford.edu

  5. 5.

    http://hadoop.apache.org

References

  1. Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466, 761–764 (2010)

    Article  Google Scholar 

  2. Blondel, V.D., Guillaume, J., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. 10008, 1-Ű12 (2008)

    Google Scholar 

  3. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)

    Article  Google Scholar 

  4. Cormen, T.H., Leiserson, C.E., et al.: Introduction to Algorithms, 2nd edn. MIT, Cambridge (2001)

    MATH  Google Scholar 

  5. Danon, L., Duch, J., et al.: Comparing community structure identification. J. Stat. Mech. 9008, 09008 (2005)

    Article  Google Scholar 

  6. Dongen, S.V.: Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht (2000)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  8. Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104, 36–41 (2007)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Hu, Y., Chen, H., et al.: Comparative definition of community and corresponding identifying algorithm. Phys. Rev. E 78, 026121 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Leung, I.X.Y., Hui, P., et al.: Towards real-time community detection in large networks. Phys. Rev. E 79, 066107 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)

    Article  Google Scholar 

  15. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)

    Article  Google Scholar 

  16. Radicchi, F., Castellano, C., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. 101, 2658–2663 (2004)

    Article  Google Scholar 

  17. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)

    Article  Google Scholar 

  18. Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)

    Article  MathSciNet  Google Scholar 

  19. Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. 100, 12123–12128 (2003)

    Article  Google Scholar 

  20. Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques. In: COCOON, pp. 161–170. Springer, Berlin (2004)

    Google Scholar 

  21. Ye, Q., Wu, B., et al: TeleComVis: exploring temporal communities in telecom networks. In: ECML PKDD, pp. 755–758. Springer, Heidelberg (2009)

    Google Scholar 

  22. Ye, Q., Wu, B., et al: Detecting communities in massive networks based on local community attractive force optimization. In: International Conference on Advances in Social Network Analysis and Mining, pp. 291–295. IEEE Computer Society, Los Alamitos (2010)

    Google Scholar 

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Acknowledgements

We thank M. E. J. Newman, Alex Arenas and Jure Leskovec for providing us the network data sets. This work is supported by the National Science Foundation of China (No. 90924029, 60905025, 61074128). It is also supported the National Hightech R&D Program of China (No.2009AA04Z136).

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qi Ye, Bin Wu & Bai Wang

Authors
  1. Qi Ye
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  2. Bin Wu
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  3. Bai Wang
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Corresponding author

Correspondence to Qi Ye .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, TOBB University, Sogutozu Cad No. 43, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Computer Science, University of Calgary, University Dr. NW 2500, Calgary, T2N 1N4, Canada

    Jon Rokne

  3. IPSC, European Commission Joint Research Cent., Via Enrico Fermi 2749, Ispra, 21027, Italy

    Gerhard Wagner

  4. De Wetstraat 16, Leiden, 2332 XT, Netherlands

    Arno H.P. Reuser

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Ye, Q., Wu, B., Wang, B. (2013). Detecting Communities in Massive Networks Efficiently with Flexible Resolution. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_16

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  • DOI: https://doi.org/10.1007/978-3-7091-1346-2_16

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1345-5

  • Online ISBN: 978-3-7091-1346-2

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