Community Detection Through Topic Modeling in Social Networks

  • Imane Tamimi
  • El Khadir Lamrani
  • Mohamed El Kamili
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10542)


The research on communities in social networks takes many paths in the literature, among which: the problematic of accurately detecting communities; modeling the evolution of those communities within the evolving network; and then finding the patterns that characterize this evolution over time. In our work, we focused on the problematic of detecting communities in social networks based on the information disseminated among users of the social network and the type of content shared by these users. The work at hand consists of a brief introduction to the subject and the problem definition, then we move to state the main contribution of our work which consists of a multi-layer model to detect communities of users based on the content shared by users, the lowest layer would detect topics of interest of each user while the upper layer would form communities from generated topics. We conclude the paper stating our perspectives and future works.


Community detection Topic modeling Social networks 


  1. 1.
    Abdelbary, H.A.: Semantic topics modeling approach for community detection 81(6), 50–58 (2013)Google Scholar
  2. 2.
    Alghamdi, R., Alfalqi, K.: A survey of topic modeling in text mining. IJACSA Int. J. Adv. Comput. Sci. Appl. 6(1), 147–153 (2015)Google Scholar
  3. 3.
    Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., Robinson, T.: One billion word benchmark for measuring progress in statistical language modeling. Technical report, Google (2013)Google Scholar
  4. 4.
    Das, R., Zaheer, M., Dyer, C.: Gaussian LDA for topic models with word embeddings. Proc. ACL 2015, 795–804 (2015)Google Scholar
  5. 5.
    Fortunato, S., Castellano, C.: Community structure in graphs. In: Computational Complexity. Theory, Techniques, and Applications 9781461418, pp. 490–512 (2012)Google Scholar
  6. 6.
    Henry, T., Banks, D., Chai, C., Owens-Oas, D.: Modeling community structure and topics in dynamic text networks. arXiv Preprint (2016)
  7. 7.
    Hinton, G.E.: A practical guide to training restricted boltzmann machines a practical guide to training restricted Boltzmann machines. Comput. (Long. Beach. Calif.) 9(3), 1 (2010)Google Scholar
  8. 8.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)CrossRefMATHGoogle Scholar
  9. 9.
    Larochelle, H.: Classification using discriminative restricted Boltzmann machines (2008)Google Scholar
  10. 10.
    Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Conference on World Wide Web WWW, pp. 631–640 (2010)Google Scholar
  11. 11.
    Liu, G.: Community structure and detection in complex networks: a survey. Cs.Gsu.Edu (2012)Google Scholar
  12. 12.
    Liu, Y., Liu, Z., Chua, T.S., Sun, M.: Topical word embeddings, pp. 2418–2424 (2015)Google Scholar
  13. 13.
    Maaloe, L., Arngren, M., Imm, O.W.I., Dk, D.T.U.: Deep belief nets for topic modeling workshop on knowledge-powered deep learning for text mining arXiv:1501. 04325v1 [cs. CL ] 18 32 (2014)., January 2015
  14. 14.
    Maaloe, L., Arngren, M., Winther, O.: Deep belief nets for topic modeling, 32 (2015)Google Scholar
  15. 15.
    Campr, M., Ježek, K.: Comparing semantic models for evaluating automatic document summarization. In: Král, P., Matoušek, V. (eds.) TSD 2015. LNCS, vol. 9302, pp. 252–260. Springer, Cham (2015). doi: 10.1007/978-3-319-24033-6_29 CrossRefGoogle Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 1–9 (2013)Google Scholar
  17. 17.
    Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)Google Scholar
  18. 18.
    Pathak, N., DeLong, C., Erickson, K., Banerjee, A.: Social topic models for community extraction. In: 2nd SNA-KDD Workshop 2008 (2008)Google Scholar
  19. 19.
    Reihanian, A., Minaei-Bidgoli, B., Alizadeh, H.: Topic-oriented community detection of rating-based social networks. J. King Saud Univ. - Comput. Inf. Sci. pp. 1–8 (2015)Google Scholar
  20. 20.
    Reihanian, A., Minaei-Bidgoli, B., Alizadeh, H.: Topic-oriented community detection of rating-based social networks. J. King Saud Univ. - Comput. Inf. Sci. 28(3), 303–310 (2016)Google Scholar
  21. 21.
    Salakhutdinov, R., Hinton, G.: Replicated softmax: an undirected topic model, pp. 1–8Google Scholar
  22. 22.
    Srivastava, N., Hinton, G.: Modeling documents with a deep Boltzmann machineGoogle Scholar
  23. 23.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43:1–43:35 (2013)CrossRefMATHGoogle Scholar
  24. 24.
    Zhu, R., Jiang, W.: Combing random walks and nonparametric bayesian topic model for community detection, pp. 1–13 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Imane Tamimi
    • 1
  • El Khadir Lamrani
    • 2
  • Mohamed El Kamili
    • 3
  1. 1.LIMS, FSDMSidi Mohammed Ben Abdellah UniversityFesMorocco
  2. 2.LTIM, FSBMHassan II UniversityFesMorocco
  3. 3.LIMS, FSDMSidi Mohammed Ben Abdellah UniversityFesMorocco

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