Skip to main content
SpringerLink
Log in

Overlapping Community Detection Algorithm Based on Spectral and Fuzzy C-Means Clustering

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 917))

  • 923 Accesses

Abstract

Community detection is the detection and revelation of the communities inherent in different types of complex networks, which can help people understand various functions and hidden rules of the complex networks to predict their future behavior. The spectral clustering algorithm suffers from the disadvantage of spending too much time for calculating eigenvectors, so it can’t apply in large-scale networks. This paper puts forward the overlapping community detection algorithm devised upon spectral with Fuzzy c-means clustering. Firstly, the node similarity is calculated according to the influence of attribute features on nodes. Secondly, the node similarity is combined with the Jaccard similarity to construct the similarity matrix. Thirdly, the feature decomposition is performed on the matrix by using the DPIC (Deflation-based power iteration clustering) method. Finally, the advanced version of the traditional Fuzzy c-means algorithm can find the overlapping communities. The results of experiments reveal that it can detect communities on real and artificial datasets effectively and accurately.

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 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

Institutional subscriptions

References

  1. Newman, M.E.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2), 404–409 (2001)

    Article  MathSciNet  Google Scholar 

  2. Takaffoli, M., Sangi, F., Fagnan, J., et al.: Community evolution mining in dynamic social networks. Procedia - Soc. Behav. Sci. 22(22), 49–58 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  5. Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA 101(9), 2658 (2004)

    Article  Google Scholar 

  6. Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 70(2), 6111 (2004)

    Google Scholar 

  7. Luxburg, U.V.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  8. Jaccard, P.: Etude de la distribution florale dans une portion des Alpes et du Jura. Bulletin De La Societe Vaudoise Des Sciences Naturelles 37(142), 547–579 (1901)

    Google Scholar 

  9. Havens, T.C., Bezdek, J.C., Leckie, C., et al.: Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Syst. 20(6), 1130–1146 (2012)

    Article  Google Scholar 

  10. The, A.P., Thang, N.D., Vinh, L.T., et al.: Deflation-based power iteration clustering. In: IASTED International Conference on Communications, Internet, and Information Technology, pp. 73–78. ACTA Press (2007). https://doi.org/10.1007/s10489-012-0418-0

  11. Miller, F.P., Vandome, A.F., Mcbrewster, J.: Gaussian Function (2010)

    Google Scholar 

  12. Sonka, M., Hlavac, V., Ceng, R.B.D.M.: Image processing, analysis and machine vision. J. Electron. Imag. 19(82), 685–686 (2008)

    Google Scholar 

  13. Szeliski, R.: Computer vision: algorithms and applications. Springer, New York (2010)

    MATH  Google Scholar 

  14. 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), 1–35 (2013)

    Article  Google Scholar 

  15. Mcauley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: International Conference on Neural Information Processing Systems, pp. 539–547. Curran Associates Inc. (2012)

    Google Scholar 

  16. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E: Stat. Nonlinear Soft Matter Phys. 78(4 Pt 2), 046110 (2008)

    Article  Google Scholar 

  17. Guo, K., Guo, W.Z., Qiu, Q.R., et al.: Community detection algorithm based on local affinity propagation and user profile. J. Commun. 36(2), 68–79 (2015)

    Google Scholar 

  18. Shen, H., Cheng, X., Cai, K., et al.: Detect overlapping and hierarchical community structure in networks. Phy. A Stat. Mech. Appl. 388(8), 1706–1712 (2009)

    Article  Google Scholar 

  19. Danon, L., DĂ­azguilera, A., Duch, J., et al.: Comparing community structure identification. J. Stat. Mech. 2005(09), 09008 (2005)

    Article  Google Scholar 

  20. Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016)

    Google Scholar 

  21. Liang, L.: Network representation learning and link prediction towards attributed network. East China Normal University (2017)

    Google Scholar 

  22. Tu, C.C., Yang, C., Liu, Z.Y., et al.: Network representation learning: an overview (in Chinese). Sci. Sin. Inf. (2017). https://doi.org/10.1360/n112017-00145

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61300104, 61300103 and 61672158, the Fujian Province High School Science Fund for Distinguished Young Scholars under Grant No. JA12016, the Program for New Century Excellent Talents in Fujian Province University under Grant No. JA13021, the Fujian Natural Science Funds for Distinguished Young Scholar under Grant Nos. 2014J06017 and 2015J06014, the Major Production and Research Project of Fujian Scientific and Technical Department, the Technology Innovation Platform Project of Fujian Province (Grants Nos. 2009J1007, 2014H2005), the Fujian Collaborative Innovation Center for Big Data Applications in Governments, and the Natural Science Foundation of Fujian Province under Grant Nos. 2013J01230 and 2014J01232, Industry-Academy Cooperation Project under Grant Nos. 2014H6014 and 2017H6008. Haixi Government Big Data Application Cooperative Innovation Center.

Author information

Authors and Affiliations

  1. College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou, 350116, China

    Xiaoshan He & Kun Guo

  2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China

    Xiaoshan He & Kun Guo

  3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China

    Xiaoshan He & Kun Guo

  4. Power Science and Technology Corporation State Grid Information and Telecommunication Group, Xiamen, 351008, China

    Qinwu Liao & Qiaoling Yan

Authors
  1. Xiaoshan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinwu Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiaoling Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Guo .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. University of Shanghai for Science and Technology, Shanghai , China

    Liping Gao

  5. Tongji University, Shanghai, China

    Hongfei Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, X., Guo, K., Liao, Q., Yan, Q. (2019). Overlapping Community Detection Algorithm Based on Spectral and Fuzzy C-Means Clustering. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3044-5_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3043-8

  • Online ISBN: 978-981-13-3044-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation