Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Neural Networks

ICANN 2016: Artificial Neural Networks and Machine Learning – ICANN 2016 pp 216–224Cite as

Comparison of Methods for Community Detection in Networks

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9887))

Abstract

Community detection refers to extracting dense interacting nodes or subgraphs that form relevant aggregation (aka, communities) within networks. We present nine community detection methods based on different approaches, and we compare them on the Girvan-Newman community detection benchmark network. Two methods proposed by our group using spectral graph theory and fuzzy clustering obtain the best experimental results evaluated using the Omega Index.

Work partially funded by a grant of the University of Genoa.

This is a preview of subscription content, 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 EPUB and 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

Learn about institutional subscriptions

References

  1. Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

    Book  MATH  Google Scholar 

  3. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  4. Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  MATH  Google Scholar 

  5. Collins, L.M., Dent, C.W.: Omega: a general formulation of the rand index of cluster recovery suitable for non-disjoint solutions. Multivar. Behav. Res. 23(2), 231–242 (1988)

    Article  Google Scholar 

  6. Donetti, L., Munoz, M.A.: Detecting network communities: a new systematic and efficient algorithm. J. Stat. Mech. Theor. Exp. 2004(10), P10012 (2004)

    Article  MATH  Google Scholar 

  7. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  8. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gregory, S.: An algorithm to find overlapping community structure in networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 91–102. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)

    Article  MATH  Google Scholar 

  11. Jonsson, P.F., Cavanna, T., Zicha, D., Bates, P.A.: Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis. BMC Bioinform. 7(1), 1 (2006)

    Article  Google Scholar 

  12. Krishnapuram, R., Keller, J.M.: The possibilistic c-means algorithm: insights and recommendations. IEEE Trans. Fuzzy Sys. 4(3), 385–393 (1996)

    Article  Google Scholar 

  13. Mahmoud, H., Masulli, F., Rovetta, S., Russo, G.: Community detection in protein-protein interaction networks using spectral and graph approaches. In: Formenti, E., Tagliaferri, R., Wit, E. (eds.) CIBB 2013. LNCS, vol. 8452, pp. 62–75. Springer, Heidelberg (2014)

    Google Scholar 

  14. Newman, M.E.: Detecting community structure in networks. Eur. Phys. J. B-Condens. Matter Complex Sys. 38(2), 321–330 (2004)

    Article  Google Scholar 

  15. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  16. Nepusz, T., Petróczi, A., Négyessy, L., Bazsó, F.: Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E 77(1), 016107 (2008)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  18. Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Proc. Syst. 2, 849–856 (2002)

    Google Scholar 

  19. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  20. Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  21. Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43 (2013)

    Article  MATH  Google Scholar 

  22. Zhang, S., Wang, R.-S., Zhang, X.-S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Phys. A Stat. Mech. Appl. 374(1), 483–490 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIBRIS - Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Dodecaneso 35, 16146, Genoa, Italy

    Hassan Mahmoud, Francesco Masulli, Stefano Rovetta & Amr Abdullatif

  2. Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, PA, USA

    Francesco Masulli

Authors
  1. Hassan Mahmoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Masulli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Rovetta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amr Abdullatif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Masulli .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Mahmoud, H., Masulli, F., Rovetta, S., Abdullatif, A. (2016). Comparison of Methods for Community Detection in Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44781-0_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44780-3

  • Online ISBN: 978-3-319-44781-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation