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Label propagation algorithm for community discovery based on centrality and common neighbours

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Abstract

We propose a label propagation-based algorithm to extract community structure using a new similarity measure based on centrality and common neighbours. Initially, a distinct label is assigned to each vertex. Along the process, each vertex adopts the label that most of its similar neighbours have. Communities are defined when closely related groups of vertices agree on a single label. Experiments on both real-world and synthetic networks show that our algorithm has better stability than the label propagation algorithm and finds communities with small sizes and indistinct boundaries more effectively than most state-of-the-art community detection methods.

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Data availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

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

    Article  MathSciNet  Google Scholar 

  2. Karataş A, Şahin S (2018) Application areas of community detection: a review. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp 65–70. https://doi.org/10.1109/IBIGDELFT.2018.8625349

  3. Elezaj O, Yayilgan SY, Kalemi E (2021) Criminal network community detection in social media forensics. In: Yildirim Yayilgan S, Bajwa IS, Sanfilippo F (eds) Intelligent technologies and applications. Springer, Cham, pp 371–383

    Google Scholar 

  4. Cao C, Ni Q, Zhai Y (2015) An improved collaborative filtering recommendation algorithm based on community detection in social networks. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO ’15, Association for Computing Machinery, New York, pp 1–8. https://doi.org/10.1145/2739480.2754670

  5. Yudhoatmojo SB, Samuar MA (2017) Community detection on citation network of dblp data sample set using linkrank algorithm. Proced Comput Sci 124:29–37. https://doi.org/10.1016/j.procs.2017.12.126

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133. https://doi.org/10.1103/PhysRevE.69.066133

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. 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 

  10. Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: Yolum P, Güngör T, Gürgen F, Özturan C (eds) Computer and information sciences - ISCIS. Springer, Berlin, Heidelberg, pp 284–293

    Google Scholar 

  11. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123. https://doi.org/10.1073/pnas.0706851105

    Article  Google Scholar 

  12. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  Google Scholar 

  13. Jabbour S, Mhadbhi N, Raddaoui B, Sais L (2018) Triangle-driven community detection in large graphs using propositional satisfiability. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp 437–444. https://doi.org/10.1109/AINA.2018.00072

  14. Jabbour S, Mhadhbi N, Raddaoui B, Sais L (2020) Sat-based models for overlapping community detection in networks. Computing 102(5):1275–1299. https://doi.org/10.1007/s00607-020-00803-y

    Article  MathSciNet  Google Scholar 

  15. Koc I (2022) A fast community detection algorithm based on coot bird metaheuristic optimizer in social networks. Eng Appl Artif Intell 114:105202. https://doi.org/10.1016/j.engappai.2022.105202

    Article  Google Scholar 

  16. Belkhiri Y, Kamel N, Drias H (2022) An efficient multi-swarm elephant herding optimization for solving community detection problem in complex environment. Concurr Comput Pract Exp 34(3):6590

    Article  Google Scholar 

  17. Guendouz M, Amine A, Hamou RM (2017) A discrete modified fireworks algorithm for community detection in complex networks. Appl Intell 46(2):373–385

    Article  Google Scholar 

  18. Messaoudi I, Kamel N (2019) Community detection using fireworks optimization algorithm. Int J Artif Intell Tools 28(03):1950010

    Article  Google Scholar 

  19. Ballal A, Kion-Crosby WB, Morozov AV (2022) Network community detection and clustering with random walks. Phys Rev Res 4:043117. https://doi.org/10.1103/PhysRevResearch.4.043117

    Article  Google Scholar 

  20. Gui Q, Deng R, Xue P, Cheng X (2018) A community discovery algorithm based on boundary nodes and label propagation. Pattern Recogn Lett 109:103–109. https://doi.org/10.1016/j.patrec.2017.12.018

    Article  Google Scholar 

  21. Wang T, Chen S, Wang X, Wang J (2020) Label propagation algorithm based on node importance. Phys A 551:124137

    Article  Google Scholar 

  22. Malhotra D, Chug A (2021) A modified label propagation algorithm for community detection in attributed networks. Int J Inf Manag Data Insights 1(2):100030. https://doi.org/10.1016/j.jjimei.2021.100030

    Article  Google Scholar 

  23. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174. https://doi.org/10.1016/j.physrep.2009.11.002

    Article  MathSciNet  Google Scholar 

  24. Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44. https://doi.org/10.1016/j.physrep.2016.09.002

    Article  MathSciNet  Google Scholar 

  25. Garza SE, Schaeffer SE (2019) Community detection with the label propagation algorithm: a survey. Phys A Stat Mech Appl 534:122058. https://doi.org/10.1016/j.physa.2019.122058

    Article  MathSciNet  Google Scholar 

  26. Lin Z, Zheng X, Xin N, Chen D (2014) Ck-lpa: efficient community detection algorithm based on label propagation with community kernel. Phys A Stat Mech Appl 416:386–399. https://doi.org/10.1016/j.physa.2014.09.023

    Article  Google Scholar 

  27. Lou H, Li S, Zhao Y (2013) Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Phys A 392(14):3095–3105. https://doi.org/10.1016/j.physa.2013.03.014

    Article  Google Scholar 

  28. Jokar E, Mosleh M (2019) Community detection in social networks based on improved label propagation algorithm and balanced link density. Phys Lett A 383(8):718–727. https://doi.org/10.1016/j.physleta.2018.11.033

    Article  MathSciNet  Google Scholar 

  29. Šubelj L, Bajec M (2011) Robust network community detection using balanced propagation. Eur Phys J B 81(3):353–362. https://doi.org/10.1140/epjb/e2011-10979-2

    Article  Google Scholar 

  30. Sun H, Huang J, Zhong X, Liu K, Zou J, Song Q (2014) Label propagation with \(\alpha \)-degree neighborhood impact for network community detection. Comput Intell Neurosci 2014:45–45

    Article  Google Scholar 

  31. Laassem B, Idarrou A, Boujlaleb L, Iggane M (2022) Label propagation algorithm for community detection based on coulomb’s law. Phys A Stat Mech Appl 593:126881. https://doi.org/10.1016/j.physa.2022.126881

    Article  Google Scholar 

  32. Das K, Samanta S, Pal M (2018) Study on centrality measures in social networks: a survey. Soc Netw Anal Min 8(1):13. https://doi.org/10.1007/s13278-018-0493-2

    Article  Google Scholar 

  33. Fortunato S, Newman ME (2022) 20 years of network community detection. Nat Phys 18(8):848–850

    Article  Google Scholar 

  34. Traag VA, Waltman L, Eck NJ (2019) From louvain to leiden: guaranteeing well-connected communities. Sci Rep 9(1):5233. https://doi.org/10.1038/s41598-019-41695-z

    Article  Google Scholar 

  35. Tunali V (2021) Large-scale network community detection using similarity-guided merge and refinement. IEEE Access 9:78538–78552. https://doi.org/10.1109/ACCESS.2021.3083971

    Article  Google Scholar 

  36. Zhang X-K, Tian X, Li Y-N, Song C (2014) Label propagation algorithm based on edge clustering coefficient for community detection in complex networks. Int J Mod Phys B 28(30):1450216

    Article  MathSciNet  Google Scholar 

  37. Steinhaeuser K, Chawla NV (2008) Community detection in a large real-world social network. In: Liu H, Salerno JJ, Young MJ (eds) Social computing, behavioral modeling and prediction. Springer, Boston, MA, pp 168–175

    Chapter  Google Scholar 

  38. Huang B, Wang C, Wang B (2019) Nmlpa: uncovering overlapping communities in attributed networks via a multi-label propagation approach. Sensors 19(2):260

    Article  Google Scholar 

  39. Murata T, Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. In: IEEE/WIC/ACM International Conference on Web Intelligence (WI’07), pp 85–88 . https://doi.org/10.1109/WI.2007.52

  40. Traag VA, Šubelj L (2023) Large network community detection by fast label propagation. Sci Rep 13(1):2701. https://doi.org/10.1038/s41598-023-29610-z

    Article  Google Scholar 

  41. Chen T, Singh P, Bassler KE (2018) Network community detection using modularity density measures. J Stat Mech Theory Exp 2018(5):053406

    Article  MathSciNet  Google Scholar 

  42. Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6(1):1–18

    Google Scholar 

  43. Emmons S, Kobourov S, Gallant M, Börner K (2016) Analysis of network clustering algorithms and cluster quality metrics at scale. PLoS ONE 11(7):1–18. https://doi.org/10.1371/journal.pone.0159161

    Article  Google Scholar 

  44. Tandon A, Albeshri A, Thayananthan V, Alhalabi W, Radicchi F, Fortunato S (2021) Community detection in networks using graph embeddings. Phys Rev E 103:022316. https://doi.org/10.1103/PhysRevE.103.022316

    Article  Google Scholar 

  45. Dao VL, Bothorel C, Lenca P (2020) Community structure: a comparative evaluation of community detection methods. Netw Sci 8(1):1–41. https://doi.org/10.1017/nws.2019.59

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Fred ALN, Jain AK (2003) Robust data clustering. In 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2

  48. Danon L, Díaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 2005(09):09008–09008. https://doi.org/10.1088/1742-5468/2005/09/p09008

    Article  Google Scholar 

  49. Rossetti G, Pappalardo L, Rinzivillo S (2016) A novel approach to evaluate community detection algorithms on ground truth. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds.) Complex Networks VII: Proceedings of the 7th Workshop on Complex Networks CompleNet 2016, Springer, Cham, pp 133–144 https://doi.org/10.1007/978-3-319-30569-1_10

  50. Newman M (2018) 158measures and metrics. Oxford University Press, Oxford. https://doi.org/10.1093/oso/9780198805090.003.0007

    Book  Google Scholar 

  51. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473. https://doi.org/10.1086/jar.33.4.3629752

    Article  Google Scholar 

  52. Lusseau D, Schneider K, Boisseau O, Haase P, Slooten E, Dawson S (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations - Can geographic isolation explain this unique trait? Behav Ecol Sociobiol 54:396–405. https://doi.org/10.1007/s00265-003-0651-y

    Article  Google Scholar 

  53. Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213. https://doi.org/10.1007/s10115-013-0693-z

    Article  Google Scholar 

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

    Article  Google Scholar 

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Funding

The authors did not receive support from any organisation for the submitted work.

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Authors and Affiliations

  1. LRSD, Department of Computer Science, University Ferhat Abbas Setif 1, Setif, Algeria

    Asma Douadi & Nadjet Kamel

  2. CRIL CNRS, Université d’Artois, Lens, France

    Lakhdar Sais

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  1. Asma Douadi
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  2. Nadjet Kamel
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  3. Lakhdar Sais
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Asma Douadi. The first draft of the manuscript was written by Asma Douadi, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Asma Douadi.

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Douadi, A., Kamel, N. & Sais, L. Label propagation algorithm for community discovery based on centrality and common neighbours. J Supercomput 80, 11816–11842 (2024). https://doi.org/10.1007/s11227-024-05904-5

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