Skip to main content
SpringerLink
Log in

A stable community detection approach for complex network based on density peak clustering and label propagation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm based on density peak clustering and label propagation (DS-LPA). First, the local density calculation method in density peak clustering algorithm is improved in finding the community center of the network, so as to build a suitable initial community, which can improve the quality of community partition. Then, the label update order is determined reasonably by computing the information transmission power of nodes, and the solutions for multiple candidate labels are provided, which greatly improved the robustness of the algorithm. DS-LPA is compared with other seven algorithms on the synthetic network and real-world networks. NMI, ARI, and modularity are used to evaluate these algorithms. It can be concluded that DS-LPA has a higher performance than most comparison algorithms on synthetic network with ten different mixed parameters by statistical testing. And DS-LPA can quickly calculate the best community partition on different sizes of real-world networks.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. https://github.com/Lichuanwei1996/DS-LPA

  2. https://www.santofortunato.net/resources

  3. http://www-personal.umich.edu/~mejn/netdata/

  4. http://snap.stanford.edu/data/#socnets

References

  1. Guerrero M, Montoya FG, Baños R, Alcayde A, Gil C (2018) Community detection in national-scale high voltage transmission networks using genetic algorithms. Adv Eng Inform 38:232– 241

    Article  Google Scholar 

  2. Lu F, Liu K, Duan Y, Cheng S, Du F (2018) Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach. Physica A: Stat Mech Appl 501:227–237

    Article  Google Scholar 

  3. Mallek S, Boukhris I, Elouedi Z (2015) Community detection for graph-based similarity: application to protein binding pockets classification. Pattern Recogn Lett 62:49–54

    Article  Google Scholar 

  4. Pattanayak HS, Sangal AL, Verma HK (2019) Community detection in social networks based on fire propagation. Swarm Evol Comput 44:31–48

    Article  Google Scholar 

  5. M’barek MB, Borgi A, Bedhiafi W, Hmida SB (2018) Genetic algorithm for community detection in biological networks. Procedia Comput Sci 126:195–204

    Article  Google Scholar 

  6. Sun PG, Gao L, Yang Y (2013) Maximizing modularity intensity for community partition and evolution. Inf Sci 236:83– 92

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  8. Feng Y, Chen H, Li T, Luo C (2020) A novel community detection method based on whale optimization algorithm with evolutionary population. Appl Intell:1–20

  9. Guo K, He L, Chen Y, Guo W, Zheng J (2020) A local community detection algorithm based on internal force between nodes. Appl Intell 50(2):328–340

    Article  Google Scholar 

  10. Huang X, Cheng H, Yu JX (2015) Dense community detection in multi-valued attributed networks. Inf Sci 314:77–99

    Article  MATH  Google Scholar 

  11. Li H J, Bu Z, Li A, Liu Z, Shi Y (2016) Fast and accurate mining the community structure: Integrating center locating and membership optimization. IEEE Trans Knowl Data Eng 28(9):2349–2362

    Article  Google Scholar 

  12. Plantié M, Crampes M (2013) Survey on social community detection. In: Social media retrieval. Springer, pp 65–85

  13. Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6:30750

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  15. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exper 2008(10):P10008

    Article  MATH  Google Scholar 

  16. Derényi I, Palla G, Vicsek T (2005) Clique percolation in random networks. Phys Rev Lett 94(16):160202

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Bagrow JP, Bollt EM (2005) Local method for detecting communities. Phys Rev E 72 (4):046108

    Article  Google Scholar 

  19. 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 

  20. Lou H, Li S, Zhao Y (2013) Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Physica A: Stat Mech Appl 392(14):3095– 3105

    Article  Google Scholar 

  21. Zarandi FD, Rafsanjani MK (2018) Community detection in complex networks using structural similarity. Physica A: Stat Mech Appl 503:882–891

    Article  Google Scholar 

  22. Nan DY, Yu W, Liu X, Zhang YP, Dai WD (2018) A framework of community detection based on individual labels in attribute networks. Physica A: Stat Mech Appl 512:523–536

    Article  Google Scholar 

  23. Xing Y, Meng F, Zhou Y, Zhu M, Shi M, Sun G (2014) A node influence based label propagation algorithm for community detection in networks. Sci World J 2014:1–13

    Google Scholar 

  24. Zhang XK, Ren J, Song C, Jia J, Zhang Q (2017) Label propagation algorithm for community detection based on node importance and label influence. Phys Lett A 381(33):2691– 2698

    Article  MathSciNet  MATH  Google Scholar 

  25. Leung IX, Hui P, Lio P, Crowcroft J (2009) Towards real-time community detection in large networks. Phys Rev E 79(6): 066107

    Article  Google Scholar 

  26. Qiang H, Yan G (2012) A method of personalized recommendation based on multi-label propagation for overlapping community detection. In: 2012 3Rd international conference on system science, engineering design and manufacturing informatization. IEEE, vol 1, pp 360–364

  27. Liu T, Yan X, Shenzhi C (2018) Site-effect-based semi-synchronous label propagation algorithm for community detection. In: 2018 IEEE Intl conf on parallel & distributed processing with applications, ubiquitous computing & communications, big data & cloud computing, social computing & networking, sustainable computing & communications, ISPA/IUCC/ BDCloud/SocialCom/SustainCom. IEEE, pp 215–222

  28. Barber MJ, Clark JW (2009) Detecting network communities by propagating labels under constraints. Phys Rev E 80(2):026129

    Article  Google Scholar 

  29. Liu X, Murata T (2010) Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Physica A: Stat Mech Appl 389(7):1493–1500

    Article  Google Scholar 

  30. Le BD, Shen H, Nguyen H, Falkner N (2019) Improved network community detection using meta-heuristic based label propagation. Appl Intell 49(4):1451–1466

  31. Zhang W, Zhang R, Shang R, Jiao L (2018) Weighted compactness function based label propagation algorithm for community detection. Physica A: Stat Mech Appl 492:767–780

    Article  Google Scholar 

  32. Lin Z, Zheng X, Xin N, Chen D (2014) Ck-lpa: Efficient community detection algorithm based on label propagation with community kernel. Physica A: Stat Mech Appl 416:386–399

    Article  Google Scholar 

  33. Joghan HS, Bagheri A (2017) Local edge betweenness based label propagation for community detection in complex networks. In: 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, pp 864–869

  34. Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344 (6191):1492–1496

    Article  Google Scholar 

  35. Bai X, Yang P, Shi X (2017) An overlapping community detection algorithm based on density peaks. Neurocomputing 226:7–15

    Article  Google Scholar 

  36. JIN Z, XU P (2018) An adaptive community detection algorithm of density peak clustering. J Harbin Inst Technol 50(5): 44–51

    Google Scholar 

  37. Xu M, Li Y, Li R, Zou F, Gu X (2019) Eadp: an extended adaptive density peaks clustering for overlapping community detection in social networks. Neurocomputing 337:287–302

    Article  Google Scholar 

  38. Deng ZH, Qiao HH, Gao MY, Song Q, Gao L (2019) Complex network community detection method by improved density peaks model. Physica A: Stat Mech Appl 526:121070

    Article  Google Scholar 

  39. Chen Y, Zhao P, Li P, Zhang K, Zhang J (2016) Finding communities by their centers. Sci Rep 6:24017

    Article  Google Scholar 

  40. Ding J, He X, Yuan J, Chen Y, Jiang B (2018) Community detection by propagating the label of center. Physica A: Stat Mech Appl 503:675–686

    Article  Google Scholar 

  41. Cheng Q, Liu Z, Huang J, Cheng G (2016) Community detection in hypernetwork via density-ordered tree partition. Appl Math Comput 276:384–393

    MathSciNet  MATH  Google Scholar 

  42. Šubelj L, Bajec M (2011) Robust network community detection using balanced propagation. Eur Phys J B 81(3):353–362

    Article  Google Scholar 

  43. Lü L, Zhang YC, Yeung C H, Zhou T (2011) Leaders in social networks, the delicious case. PloS one 6(6)

  44. You X, Ma Y, Liu Z (2020) A three-stage algorithm on community detection in social networks. Knowl-Based Syst 104822:187

    Google Scholar 

  45. Jiang H, Liu Z, Liu C, Su Y, Zhang X (2020) Community detection in complex networks with an ambiguous structure using central node based link prediction. Knowl-Based Syst:105626

  46. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Ence 315 (5814):972–976

    Article  MathSciNet  MATH  Google Scholar 

  47. Hubert L, Arabie P (1985) Comparing partitions. J Class 2(1):193–218

    Article  MATH  Google Scholar 

  48. Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exper 2005(09):P09008

    Article  Google Scholar 

  49. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  50. Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Lusseau D (2003) The emergent properties of a dolphin social network. Proc R Soc Lond Ser B Biol Sci 270(suppl_2):S186–S188

    Article  Google Scholar 

  53. Adamic LA, Glance N (2005) The political blogosphere and the 2004 us election: divided they blog. In: Proceedings of the 3rd international workshop on Link discovery, pp 36–43

  54. Knuth D E (1993) The stanford graphbase: a platform for combinatorial algorithms. In: SODA, vol 93, pp 41–43

  55. Watts DJ, Strogatz SH (1998) Collective dynamics of ’small-world’networks. Nature 393 (6684):440–442

    Article  MATH  Google Scholar 

  56. Jure L, Jon K, Christos F (2007) Graph evolution: Densification and shrinking diameters. ACM Transactions on Knowledge Discovery from Data

  57. Guo C, Wang J, Zhang Z (2014) Evolutionary community structure discovery in dynamic weighted networks. Physica A Stat Mech Appl 413:565–576

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China

    Chuanwei Li, Hongmei Chen, Tianrui Li & Xiaoling Yang

  2. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Chuanwei Li, Hongmei Chen, Tianrui Li & Xiaoling Yang

Authors
  1. Chuanwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongmei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianrui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoling Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongmei Chen.

Additional information

Publisher’s note

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

This work is supported by the National Natural Science Foundation of China (Nos. 61976182, 61572406, 62076171, 61876157), Key program for International S&T Cooperation of Sichuan Province (2019YFH0097), Sichuan Key R&D project (2020YFG0035).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Chen, H., Li, T. et al. A stable community detection approach for complex network based on density peak clustering and label propagation. Appl Intell 52, 1188–1208 (2022). https://doi.org/10.1007/s10489-021-02287-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02287-5

Keywords

Search

Navigation