Skip to main content
Springer Nature Link
Log in

A Common Strategy to Improve Community Detection Performance Based on the Nodes’ Property

  • Conference paper
  • First Online:
Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

  • 1136 Accesses

Abstract

In order to improve community detection results, a novel strategy based on the nodes’ property is put forward for the detecting algorithm. For a given community structure of a network, the value of the modularity will be changed when a node is moved from one community to another. Accordingly, this new strategy re-adjusts the affiliation between a node and its community to get the bigger value of the modularity. The results of community detection for some classic networks, which from Ucinet and Pajek networks, indicate that the new algorithm achieves better community structure (bigger value of modularity) than other methodologies based on modularity, such as Girvan and Newman’s algorithm, Newman’s algorithm, Aaron’s algorithm and Blondel’s algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  3. Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. NatL. Acad. Sci. U.S.A. 104(1), 36–41 (2007)

    Article  Google Scholar 

  4. Li, Z., Zhang, S., Wang, R., Zhang, X., Chen, L.: Quantitative function for community detection. Phys. Rev. E 77(2), 257–260 (2008)

    Google Scholar 

  5. Newman, M.E.J., Barabàsi, A.L., Watts, D.J.: The Structure and Dynamic of Networks. Princeton University Press, Princeton (2006)

    MATH  Google Scholar 

  6. Santo, F.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    MathSciNet  Google Scholar 

  7. White, S., and Smyth, P.: A spectral clustering approach to finding communities in graphs. In: SIAM International Conference on Data Mining. Newport Beach, California (2005)

    Google Scholar 

  8. Clauset, C., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 264–277 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Public Policy and Administration, Center for Administration Complexity Science, Xi’an Jiaotong University, Xi’an, 710049, China

    Wei Du & Xiaochen He

Authors
  1. Wei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaochen He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaochen He .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Du, W., He, X. (2016). A Common Strategy to Improve Community Detection Performance Based on the Nodes’ Property. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3614-9_43

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics