Skip to main content
SpringerLink
Log in

DPNLP: distance based peripheral nodes label propagation algorithm for community detection in social networks

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Label propagation-based methods are the most popular methods for community detection, which have a linear time complexity, thanks to the use of local features for updating node labels. However, they suffer from major concerns including instability, low accuracy, and discovering monster communities. To solve these problems, this paper proposes a novel distance based peripheral nodes label propagation algorithm for fast community detection, called DPNLP. First, core nodes are detected and their labels are distributed to the neighbors to form the initial communities. Then, labels of the peripheral nodes are identified using combinations of local features. Finally, the structures of communities are extracted after assigning a label to the nodes with degree one and two at the last stage of the method. The proposed method achieves significant speed up because of optimizing the number of required updates. In addition, DPNLP is remarkably stable and it does not have monster-community problem. According to the conducted evaluations over artificial and real-world networks, the proposed methods achieve improved results in terms of NMI, F-measure, modularity, and runtime metrics. Experiments have also been performed to confirm the stability of the algorithm and the lack of monster community’s formation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Aghaalizadeh, S., Afshord, S.T., Bouyer, A., Anari, B.: A three-stage algorithm for local community detection based on the high node importance ranking in social networks. Physica A: Stat Mech. Appl. 563, 125420 (2021)

    Article  MathSciNet  Google Scholar 

  2. Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80(2), 026129 (2009)

    Article  Google Scholar 

  3. Berahmand, K., Bouyer, A.: LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks. Int. J. Mod. Phys. B 32(06), 1850062 (2018)

    Article  Google Scholar 

  4. Berahmand, K., Bouyer, A.: A link-based similarity for improving community detection based on label propagation algorithm. J. Syst. Sci. Complexity 32(3), 737–758 (2019)

    Article  Google Scholar 

  5. Berahmand, K., Bouyer, A., Vasighi, M.: Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Trans. Comput. Soc. Syst. 5(4), 1021–1033 (2018)

    Article  Google Scholar 

  6. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  7. Bouyer, A., Roghani, H.: LSMD: A fast and robust local community detection starting from low degree nodes in social networks. Futur. Gener. Comput. Syst. 113, 41–57 (2020)

    Article  Google Scholar 

  8. Carter, K.M., Idika, N., Streilein, W.W.: Probabilistic threat propagation for network security. IEEE Trans. Inf. Forensics Secur. 9(9), 1394–1405 (2014)

    Article  Google Scholar 

  9. Chen, N., Liu, Y., Cheng, J., Liu, Q.: A novel parallel community detection scheme based on label propagation. World Wide Web 21(5), 1377–1398 (2018)

    Article  Google Scholar 

  10. 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 

  11. Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)

    Article  Google Scholar 

  12. Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in a network of human interactions. Phys. Rev. E 68(6), 065103 (2003)

    Article  Google Scholar 

  13. Kouni, I.B.E., Karoui, W., Romdhane, L.B.: Node Importance based Label propagation algorithm for overlapping community detection in networks. Expert Syst. Appl. 19, 113020 (2019)

    Google Scholar 

  14. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)

    Article  MathSciNet  Google Scholar 

  15. Liu, F., et al.: Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225 (2020)

  16. Liu, F., Wu, J., Xue, S., Zhou, C., Yang, J., Sheng, Q.: Detecting the evolving community structure in dynamic social networks. World Wide Web 23(2), 715–733 (2020)

    Article  Google Scholar 

  17. Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)

    Article  Google Scholar 

  18. Ma, T., Yue, M., Qu, J., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M.: PSPLPA: probability and similarity based parallel label propagation algorithm on spark. Physica A 503, 366–378 (2018)

    Article  Google Scholar 

  19. Ma, T., Liu, Q., Cao, J., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M.: LGIEM: Global and local node influence based community detection. Futur. Gener. Comput. Syst. 105, 533–546 (2020)

    Article  Google Scholar 

  20. Mohammadi, M., Moradi, P., Jalili, M.: SCE: Subspace-based core expansion method for community detection in complex networks. Physica A: Stat. Mech. Appl. 527, 121084 (2019)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  23. Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  24. Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  26. Rosvall, M., Bergstrom, C.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. U. S. A. 105, 1118–23 (2008)

    Article  Google Scholar 

  27. S. Stanford-Network-Analysis-Project. (2020). https://snap.stanford.edu. Accessed Oct 2020.

  28. Sasaki, Y.: The truth of the F-measure. Teach. Tutor Mater. 1(5), 1–5 (2007)

    Google Scholar 

  29. Sun, H., Liu, J., Huang, J., Wang, G., Jia, X., Song, Q.: LinkLPA: A link-based label propagation algorithm for overlapping community detection in networks. Comput. Intell. 33(2), 308–331 (2017)

    Article  MathSciNet  Google Scholar 

  30. Taheri, S., Bouyer, A.: Community detection in social networks using affinity propagation with adaptive similarity matrix. Big Data 8(3), 189–202 (2020)

    Article  Google Scholar 

  31. Tasgin, M., Bingol, H.O.: Community detection using boundary nodes in complex networks. Physica A 513, 315–324 (2019)

    Article  Google Scholar 

  32. Ting, K.M.: Precision and recall. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of machine learning, pp. 781–781. Springer US, Boston, MA (2010)

    Google Scholar 

  33. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’networks. Nature 393(6684), 440 (1998)

    Article  Google Scholar 

  34. Xing, Y., Meng, F., Zhou, Y., Zhu, M., Shi, M., Sun, G.: A node influence based label propagation algorithm for community detection in networks. Sci. World J. 2014, 627581 (2014)

    Google Scholar 

  35. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)

    Article  Google Scholar 

  36. Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)

    Article  Google Scholar 

  37. Zarezadeh, M., Nourani, E., Bouyer, A.: Community detection using a new node scoring and synchronous label updating of boundary nodes in social networks. JAIDM. 8(2), 201–212 (2020)

    Google Scholar 

  38. Zhou, H.F., Zhang, Y., Li, J.: An overlapping community detection algorithm in complex etworks based on information theory. Data Knowl. Eng. 117, 183–194 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

    Mahdi Zarezadeh, Esmaeil Nourani & Asgarali Bouyer

Authors
  1. Mahdi Zarezadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esmaeil Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asgarali Bouyer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Esmaeil Nourani.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarezadeh, M., Nourani, E. & Bouyer, A. DPNLP: distance based peripheral nodes label propagation algorithm for community detection in social networks. World Wide Web 25, 73–98 (2022). https://doi.org/10.1007/s11280-021-00966-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-021-00966-4

Keywords

Search

Navigation