[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Community Detection Algorithm Based on Local Double Rings and Fireworks Algorithm

  • Conference paper
  • First Online:
Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

In recent years, more and more algorithms have been proposed to detect communities. An improved community detection algorithm based on the concept of local double rings and the framework of fireworks algorithm (LDRFA) has been proposed in this paper. Inspired by the framework of FWA, an improved distinctive fireworks initialization strategy was given. We use this strategy to obtain a more accurate initial solution. Secondly, on the basis of fireworks algorithm, the amplitude of explosion was used to calculate the probability of changing node label. Thirdly, the mutation operator was proposed. Nodes chose labels based on the idea of LPA. Finally, tests on real-world and synthetic networks were given. The experimental results show that the proposed algorithm has better performance than existing methods in finding community structure.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Similar content being viewed by others

References

  1. John, H., et al.: Natural communities in large linked networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 541–546. ACM (2003)

    Google Scholar 

  2. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  3. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E Stat. Nonlin Soft Matter Phys. 76(2), 036106 (2007)

    Article  Google Scholar 

  4. Pizzuti, C.: GA-Net: A Genetic Algorithm for Community Detection in Social Networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). doi:10.1007/978-3-540-87700-4_107

    Chapter  Google Scholar 

  5. Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 70, 2 (2004).066111

    Google Scholar 

  6. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Nat. Acad. Sci. U.S.A 105(4), 1118–1123 (2007)

    Article  Google Scholar 

  7. Tan, Y., Zhu, Y.: Fireworks Algorithm for Optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13495-1_44

    Chapter  Google Scholar 

  8. Brin, S., Page, L.: Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)

    Article  Google Scholar 

  9. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E: Stat., Nonlin, Soft Matter Phys. 69(6), 066133 (2004)

    Article  Google Scholar 

  10. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 78(2), 046110 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhengyou Xia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ma, T., Xia, Z. (2017). A Community Detection Algorithm Based on Local Double Rings and Fireworks Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68935-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics