Skip to main content
SpringerLink
Log in

Uncovering Overlapping Community Structure

  • Conference paper
Complex Networks

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 116))

Abstract

Overlapping community structure has attracted much interest in recent years since Palla et al. proposed the k-clique percolation algorithm for community detection and pointed out that the overlapping community structure is more reasonable to capture the topology of networks. Despite many efforts to detect overlapping communities, the overlapping community problem is still a great challenge in complex networks. Here we introduce an approach to identify overlapping community structure based on an efficient partition algorithm. In our method, communities are formed by adding peripheral nodes to cores. Therefore, communities are allowed to overlap. We show experimental studies on synthetic networks to demonstrate that our method has excellent performances in community detection.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. White, S.: A spectral clustering approach to finding communities in graphs. In: SDM, pp. 43–55 (2005)

    Google Scholar 

  2. Barabasi, A.L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica a-Statistical Mechanics and Its Applications 311(3-4), 590–614 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient identification of overlapping communities. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 27–36. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  6. Derenyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physical Review Letters 94(16) (2005)

    Google Scholar 

  7. Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-organization and identification of web communities. Computer 35(3) (2002)

    Google Scholar 

  8. Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Sciences of the United States of America 104(1), 36–41 (2007)

    Article  Google Scholar 

  9. Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402(6761), 47 (1999)

    Article  Google Scholar 

  10. Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. ArXiv e-prints (2009)

    Google Scholar 

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

    Google Scholar 

  12. Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. ArXiv e-prints (February 2010)

    Google Scholar 

  13. Li, X., Liu, B., Yu, P.S.: Discovering overlapping communities of named entities. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 593–600. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Lusseau, D.: The emergent properties of a dolphin social network. Proc. Biol. Sci. 270 (suppl. 2), 186 (2003)

    Article  Google Scholar 

  15. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlin. Soft. Matter. Phys. 69(2 Pt 2), 026113 (2004)

    Article  Google Scholar 

  16. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2) (2004)

    Google Scholar 

  17. Newman, M.E.J., Girvan, M., Doyne Farmer, J.: Optimal design, robustness, and risk aversion. Phys. Rev. Lett. 89(2), 028301 (2002)

    Article  Google Scholar 

  18. Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

    Article  Google Scholar 

  19. 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)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  21. Sales-Pardo, M., Guimera, R., Moreira, A.A., Amaral, L.A.N.: Extracting the hierarchical organization of complex systems. Proceedings of the National Academy of Sciences of the United States of America 104(47), 18874–18874 (2007)

    Google Scholar 

  22. Sawardecker, E.N., Sales-Pardo, M., Amaral, L.A.N.: Detection of node group membership in networks with group overlap. European Physical Journal B 67(3), 277–284 (2009)

    Article  MATH  Google Scholar 

  23. Schuetz, P., Caflisch, A.: Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Physical Review E 77(4) (2008)

    Google Scholar 

  24. Vazquez, A., Flammini, A., Maritan, A., Vespignani, A.: Global protein function prediction from protein-protein interaction networks. Nature Biotechnology 21(6), 697–700 (2003)

    Article  Google Scholar 

  25. Wang, X.H., Jiao, L.C., Wu, J.S.: Adjusting from disjoint to overlapping community detection of complex networks. Physica a-Statistical Mechanics and Its Applications 388(24), 5045–5056 (2009)

    Article  Google Scholar 

  26. Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropologica 1(33), 452–473 (1977)

    Article  Google Scholar 

  27. Zhang, S.H., Wang, R.S., Zhang, X.S.: 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

Author information

Authors and Affiliations

  1. ENS de Lyon, 46 allée d’Italie, 69364, LyonCedex 07

    Qinna Wang & Eric Fleury

Authors
  1. Qinna Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric Fleury
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Physics at Sao Carlos, University of Sao Paulo, P.O.Box 369, 13560-970, Sao Carlos, Sao Paulo, Brazil

    Luciano da F. Costa

  2. Universidade Federal do Rio de Janeiro (UFRJ), COPPE/Federal University of Rio de Janeiro, Box 68506, 21941-972, Rio de Janeiro, RJ, Brazil

    Alexandre Evsukoff

  3. Dipartimento di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), Viale A. Doria, 6, 95125, Catania, Italy

    Giuseppe Mangioni

  4. Florida Institute of Technology, Computer Sciences, 150 W. University Blvd, 32901, Melbourne, FL, USA

    Ronaldo Menezes

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, Q., Fleury, E. (2011). Uncovering Overlapping Community Structure. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds) Complex Networks. Communications in Computer and Information Science, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25501-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25501-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25500-7

  • Online ISBN: 978-3-642-25501-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation