A Memory-Based Label Propagation Algorithm for Community Detection

  • Antonio Maria FiscarelliEmail author
  • Matthias R. Brust
  • Grégoire Danoy
  • Pascal Bouvry
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node implements memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks that MemLPA outperforms most of state-of-the-art community detection algorithms.


Network analysis Graph theory Community detection Label propagation 



This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT.


  1. 1.
    Albert, R., Jeong, H., Barabási, A.L.: Internet: Diameter of the world-wide web. Nature 401(6749), 130–131 (1999)Google Scholar
  2. 2.
    Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80(2) (2009)Google Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10) (2008)Google Scholar
  4. 4.
    Brandes, U., et al.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)Google Scholar
  5. 5.
    Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6) (2004)Google Scholar
  6. 6.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJournal Complex Syst. (2006).
  7. 7.
    Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech.: Theory Exp. 2005(09), P09008 (2005)Google Scholar
  8. 8.
    Dongen, S.: A Cluster Algorithm for Graphs (2000)Google Scholar
  9. 9.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)Google Scholar
  10. 10.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)Google Scholar
  11. 11.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.L.: The large-scale organization of metabolic networks. Nature 407(6804), 651–654 (2000)Google Scholar
  12. 12.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)Google Scholar
  13. 13.
    Leung, I.X., Hui, P., Lio, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79(6), 066107 (2009)Google Scholar
  14. 14.
    Liu, X., Murata, T.: Advanced modularity-specialized label propagation algorithm for detecting communities in networks. Phys. A Stat. Mech. 389(7), 1493–1500 (2010)Google Scholar
  15. 15.
    Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)Google Scholar
  16. 16.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)Google Scholar
  17. 17.
    Orman, G.K., Labatut, V., Cherifi, H.: Comparative evaluation of community detection algorithms: a topological approach. J. Stat. Mech. Theory Exp. 2012(08), P08001 (2012)Google Scholar
  18. 18.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: ISCIS, vol. 3733, pp. 284–293 (2005)Google Scholar
  19. 19.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)Google Scholar
  20. 20.
    Filho, R.J., Brust, M.R., Ribeiro, C.H.: Consensus dynamics in a non-deterministic naming game with shared memory. arXiv preprint arXiv:0912.4553 (2009)
  21. 21.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74(1), 016110 (2006)Google Scholar
  22. 22.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)Google Scholar
  23. 23.
    Scott, J.: Social Network Analysis. Sage, California (2017)Google Scholar
  24. 24.
    Šubelj, L., Bajec, M.: Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys. Rev. E 83(3) (2011)Google Scholar
  25. 25.
    Uzun, T.G., Da Silva-Filho, R.J., Brust, M.R., Ribeiro, C.H.: Influence of shared memory and network topology in the consensus dynamics of a naming game.
  26. 26.
    Xie, J., Szymanski, B.K.: Labelrank: A stabilized label propagation algorithm for community detection in networks. In: Network Science Workshop (NSW). IEEE (2013)Google Scholar
  27. 27.
    Xie, J., Szymanski, B.K.: Community detection using a neighborhood strength driven label propagation algorithm. arXiv preprint arXiv:1105.3264 (2011)
  28. 28.
    Xie, J., Szymanski, B.K., Liu, X.: Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: Data Mining Workshops (ICDMW). IEEE (2011)Google Scholar
  29. 29.
    Yang, Z., Algesheimer, R., Tessone, C.J.: A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, 30750 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonio Maria Fiscarelli
    • 1
    • 2
    Email author
  • Matthias R. Brust
    • 2
  • Grégoire Danoy
    • 3
  • Pascal Bouvry
    • 2
    • 3
  1. 1.C2DH, University of LuxembourgEsch-sur-AlzetteLuxembourg
  2. 2.SnT, University of LuxembourgEsch-sur-AlzetteLuxembourg
  3. 3.FSTC-CSC-ILIASUniversity of LuxembourgEsch-sur-AlzetteLuxembourg

Personalised recommendations