Skip to main content

Defining and Discovering Communities in Social Networks

  • Chapter
  • First Online:
Handbook of Optimization in Complex Networks

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 57))

Abstract

The categorization of vertices in a network is a common task across a multitude of domains. Specifically, identifying structural divisions into internally well connected sets have been shown to be useful in computer science, social science, and biology. In each of these areas, grouping vertices using structural boundaries helps one to understand the underlying processes of a network. Identifying such groupings is a non-trivial task and has been a subject of intense research in recent years.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. J. Baumes, M. Goldberg, and M. Magdon-ismail. Efficient identification of overlapping communities. In In IEEE International Conference on Intelligence and Security Informatics (ISI), pages 27–36, 2005.

    Google Scholar 

  2. J. Baumes, M. K. Goldberg, M. S. Krishnamoorthy, M. Magdon-Ismail, and N. Preston. Finding communities by clustering a graph into overlapping subgraphs, 2005.

    Google Scholar 

  3. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.

    Google Scholar 

  4. A. Clauset. Finding local community structure in networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 72(2):026132, 2005.

    Google Scholar 

  5. A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70(6):066111, 2004.

    Google Scholar 

  6. G. B. Davis and K. M. Carley. Clearing the fog: Fuzzy, overlapping groups for social networks. Social Networks, 30(3):201–212, 2008.

    Article  Google Scholar 

  7. J. Duch and A. Arenas. Community detection in complex networks using extremal optimization. Physical Review E, 72:027104, 2005.

    Article  Google Scholar 

  8. S. Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75–174, 2010.

    Article  MathSciNet  Google Scholar 

  9. M. Girvan and M. E. J. Newman. Community structure in social and biological networks. Proc Natl Acad Sci USA, 99(12):7821–6, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  10. S. Gregory. Finding overlapping communities in networks by label propagation. New J. Phys., 12, 103018, 2010.

    Article  Google Scholar 

  11. M. Goldberg, S. Kelley, M. Magdon-Ismail, K. Mertsalov, and W. A. Wallace. Communication dynamics of blog networks. In The 2nd SNA-KDD Workshop ’08 (SNA-KDD’08), August 2008.

    Google Scholar 

  12. R. Guimerà, M. Sales-Pardo, and L. A. N. Amaral. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E, 70(2):025101, 2004.

    Google Scholar 

  13. P. Jaccard. Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles, 37:547–579, 1901.

    Google Scholar 

  14. B. Karrer, E. Levina, and M. E. J. Newman. Robustness of community structure in networks. Physical Review E, 77(4):046119+, Sep 2007.

    Google Scholar 

  15. A. Lancichinetti and S. Fortunato. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E, 80(1):016118, Jul 2009.

    Google Scholar 

  16. A. Lancichinetti, S. Fortunato, and J. Kertesz. Detecting the overlapping and hierarchical community structure of complex networks. New Journal of Physics, 11, 2009.

    Google Scholar 

  17. M. E. Newman. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA, 103(23):8577–8582, 2006.

    Article  Google Scholar 

  18. V. Nicosia, G. Mangioni, V. Carchiolo, and M. Malgeri. Extending the definition of modularity to directed graphs with overlapping communities. J.STAT.MECH., page P03024, 2009.

    Google Scholar 

  19. G. Palla, A.-L. Barabasi, and T. Vicsek. Quantifying social group evolution. Nature, 446(7136):664–667, 2007.

    Article  Google Scholar 

  20. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814, 2005.

    Article  Google Scholar 

  21. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America, 101(9):2658–2663, 2004.

    Article  Google Scholar 

  22. U. N. Raghavan, R. Albert, and S. Kumara. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E, 76(3):036106, Sep 2007.

    Google Scholar 

  23. J. Reichardt and S. Bornholdt. Statistical mechanics of community detection. Phys. Rev. E, 74(1):016110, Jul 2006.

    Google Scholar 

  24. M. Rosvall and C. T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118–1123, 2008.

    Article  Google Scholar 

  25. H. Shen, X. Cheng, K. Cai, and M.-B. Hu. Detect overlapping and hierarchical community structure in networks. Physica A: Statistical Mechanics and its Applications, 388(8):1706–1712, 2009.

    Article  Google Scholar 

  26. W. Zachary. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33:452–473, 1977.

    Google Scholar 

  27. S. Zhang, R.-S. Wang, and X.-S. Zhang. Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A: Statistical Mechanics and its Applications, 374(1):483–490, 2007.

    Article  Google Scholar 

Download references

Acknowledgements

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053 and by UT-Battelle, a contractor for the U.S. Government under Department of Energy Contract DE-AC05-00OR22725. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen Kelley .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Kelley, S., Goldberg, M., Magdon-Ismail, M., Mertsalov, K., Wallace, A. (2012). Defining and Discovering Communities in Social Networks. In: Thai, M., Pardalos, P. (eds) Handbook of Optimization in Complex Networks. Springer Optimization and Its Applications(), vol 57. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-0754-6_6

Download citation

Publish with us

Policies and ethics