Skip to main content
SpringerLink
Log in

A Memetic Algorithm for Community Detection in Bipartite Networks

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

Included in the following conference series:

  • 4131 Accesses

Abstract

Community detection is a basic tool to analyze complex networks. However, there are many community detection methods for unipartite networks while just a few methods for bipartite networks (BNs). In this paper, we propose a memetic algorithm (MACD-BNs) to identify communities in BNs. We use MACD-BNs to optimize two extended measures, namely Baber modularity (Q B ) and modularity density (Q D ), on real-life and synthetic networks respectively so as to compare their performance. We conclude that Q D are more effective than Q B when the size of communities is heterogeneous while Q B is more suitable to detect communities with similar size. Besides, we also make a comparison between MACD-BNs and other community detection method and the results show the effectiveness of MACD-BNs.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Newman, M.E.: Networks: An Introduction. Oxford University Press, Oxford (2011)

    Google Scholar 

  2. Zha, H., He, X., Ding, C., Simon, H., Gu, M.: Bipartite graph partitioning and data clustering. Office of Scientific and Technical Information Technical Reports, pp. 25–32 (2001)

    Google Scholar 

  3. Guimerà, R., Sales-Pardo, M., Amaral, L.A.N.: Module identification in bipartite and directed networks. Phys. Rev. E 76(3), 036102 (2007)

    Article  Google Scholar 

  4. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2009)

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  6. Wang, X., Liu, J.: A layer reduction based community detection algorithm on multiplex networks. Phys. A 471, 244–252 (2017)

    Article  Google Scholar 

  7. Jiang, Z., Liu, J., Wang, S.: Traveling salesman problems with PageRank Distance on complex networks reveal community structure. Phys. A 463, 293–302 (2016)

    Article  MathSciNet  Google Scholar 

  8. Larremore, D.B., Clauset, A., Jacobs, A.Z.: Efficiently inferring community structure in bipartite networks. Phys. Rev. E 90(1), 012805 (2014)

    Article  Google Scholar 

  9. Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. E 76(4), 046115 (2007)

    Article  Google Scholar 

  10. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Etienne, L.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), 10008 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Barber, M.J.: Modularity and community detection in bipartite networks. Phys. Rev. E 76(6), 066102 (2007)

    Article  MathSciNet  Google Scholar 

  13. Xu, Y., Chen, L., Li, B.: Density-based modularity for evaluating community structure in bipartite networks. Inf. Sci. 317, 278–294 (2015)

    Article  MathSciNet  Google Scholar 

  14. Liu, X., Murata, T.: Community detection in large-scale bipartite networks, In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT 2009, vol. 5, pp. 50–57 (2009)

    Google Scholar 

  15. Murata, T.: Modularities for bipartite networks. In: Proceedings of the 20th ACM conference on Hypertext and hypermedia, Italy, pp. 245–250 (2009)

    Google Scholar 

  16. Suzuki, K., Wakita, K.: Extracting multi-facet community structure from bipartite networks. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 312–319 (2009)

    Google Scholar 

  17. Gong, M., Fu, B., Jiao, L.: Memetic algorithm for community detection in networks. Phys. Rev. E 84(5), 056101 (2011)

    Article  Google Scholar 

  18. Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)

    Article  Google Scholar 

  19. Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)

    Article  Google Scholar 

  20. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech: Theory Exp. 2005(09), 09008 (2005)

    Article  Google Scholar 

Download references

Acknowledgement

This work is partially supported by the Outstanding Young Scholar Program of National Natural Science Foundation of China (NSFC) under Grant 61522311, the Overseas, Hong Kong & Macao Scholars Collaborated Research Program of NSFC under Grant 61528205, and the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China under Grant 2017JZ017.

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Xiaodong Wang & Jing Liu

Authors
  1. Xiaodong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Liu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, X., Liu, J. (2017). A Memetic Algorithm for Community Detection in Bipartite Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70093-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation