Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

CILPA: a cohesion index based label propagation algorithm for unveiling communities in complex social networks

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Social network analysis is the process of recording various patterns of interactions between a set of social entities. An important phenomenon that draws the attention of analysis is the emergence of communities in these networks. The understanding and identifying communities in these networks is challenging and has gained much importance these days. There are many approaches suggested to identify communities in social networks, and most of them are time consuming when implemented or need some important user-input parameters. In this paper, a new label propagation algorithm named CILPA (Cohesion Index based Label Propagation Algorithm) is proposed. The algorithm brings in two new functions, called Cohesion Similarity (CoSim) and Cohesion Index (CI). The cohesion index function measures cohesiveness of nodes and similarity with neighbor nodes is measured using cohesion similarity. Cohesion index as the base, we propose a new label propagation algorithm with precise node update sequence and node priority. Prior information about the number of communities is not required in our algorithm and the results show that the quality of communities obtained by CILPA are very stable and better than those detected by original LPA. Results of experiments conducted on real world networks demonstrate the efficiency of our approach and indicate that cohesion index calculation of nodes improves the accuracy and CILPA is an efficient method for unveiling communities in complex social networks.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Nascimento MA, Sander J, Pound J (2003) Analysis of sigmod’s co-authorship graph. SIGMOD Rec 32(2):57–58

    Article  Google Scholar 

  2. Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. ACM SIGCOMM Comput Commun Rev 29(4):251–262

    Article  MATH  Google Scholar 

  3. J. Kleinberg (1998) Authoritative sources in a hyperlinked environment. In Proceedings of the ninth annual ACM-SIAM symposium on discrete algorithms

  4. L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan (2006) Group formation in large social networks: membership, growth, and evolution, In Proceedings of KDD, pp. 44–54

  5. Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393:440–442

    Article  MATH  Google Scholar 

  6. Williams RJ, Martinez ND (2000) Simple rules yield complex food webs. Nature 404:180–183

    Article  Google Scholar 

  7. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi A (2000) The large scale organization of metabolic networks. Nature 407:651–654

    Article  Google Scholar 

  8. LA. Adamic, N. Glance (2005) The political blogosphere and the 2004 US election: divided they blog. In Proceedings of LinkKDD ‘05. pp. 36–43

  9. Enugala R, Rajamani L, Ali K, Kurapati S et al (2016) Identifying natural communities in social networks using modularity coupled with self organizing maps. Computational intelligence in data mining—vol.1. Advances in intelligent systems and computing 410. Springer, India, pp 367–376. https://doi.org/10.1007/978-81-322-2734-2_37

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  12. E. Raju, MA. Hameed K. Sravanthi (20105) Detecting communities in social networks using unnormalized spectral clustering incorporated with bisecting K-means. In Proceedings of IEEE international conference on electrical, computer and communication technologies (ICECCT), India

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

    Article  MathSciNet  Google Scholar 

  14. Pizzuti C (2008) GA-Net: A genetic algorithm for community detection in social networks. PPSN, volume 5199. Lecture notes in computer science. Springer, Berlin, pp 1081–1090

    Google Scholar 

  15. Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307

    Article  MATH  Google Scholar 

  16. GW. Flake, S. Lawrence, CL. Giles (2000) Efficient identification of web communities. In Proceedings of the Sixth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp. 150–160

  17. S. White, P. Smyth (2005) A spectral clustering approach to finding communities in graph. In SIAM international conference on data mining, vol. 5, SIAM, pp. 76–84

  18. X. Xu, N. Yuruk, Z. Feng, TA. Schweiger (2007) Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp. 824–833

  19. H. Sun, J. Huang, J. Han, H. Deng, P. Zhao, B. Feng(2010). Gskeletonclu: density-based network clustering via structure-connected tree division or agglomeration. In IEEE international conference on data mining, IEEE, pp. 481–490

  20. Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111

    Article  Google Scholar 

  21. JE. Hopcroft, O. Khan, B. Kulis, B. Selman (2003) Natural communities in large linked networks. In Proceedings of international conference on knowledge discovery and data mining (KDD 2003), pp. 541–546

  22. Clauset A (2005) Finding local community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys 72(2):254–271

    Article  Google Scholar 

  23. M. Ester, HP. Kriegel, J. Sander, X. Xu (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In Knowledge discovery and data mining, Vol. 96, pp. 226–231

  24. M. Ankerst, MM. Breunig, HP. Kriegel, J. Sander(1999) OPTICS: Ordering points to identify the clustering structure. In International conference on management of data, vol. 28, ACM, pp. 49–60

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

    Article  Google Scholar 

  26. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133

    Article  Google Scholar 

  27. J. Xie, BK. Szymanski (2011) Community detection using a neighborhood strength driven label propagation algorithm. In IEEE network science workshop 2011, p 188–195

  28. Leung I, Hui P, Lio P, Crowcroft J (2009) Towards real-time community detection in large networks. Phys Rev E 79:066107

    Article  Google Scholar 

  29. Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12:103018

    Article  Google Scholar 

  30. J. Xie BK. Szymanski (2012) Towards linear time overlapping community detection in social networks. In PAKDD, pp 25–36

  31. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  33. Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54:396–405

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. Raju.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raju, E., Ramadevi, Y. & Sravanthi, K. CILPA: a cohesion index based label propagation algorithm for unveiling communities in complex social networks. Int. j. inf. tecnol. 10, 435–445 (2018). https://doi.org/10.1007/s41870-018-0190-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0190-4

Keywords

Navigation