Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

  • Jiuchuan Jiang
  • Manfred Jaeger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes. In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models.


Community detection Multiplex networks Relational Bayesian networks Statistical relational learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Newman, M.: Communities, Modules and Large-Scale Structure in Networks. Nature Physics 8(1), 25–31 (2011)CrossRefGoogle Scholar
  2. 2.
    Girvan, M., Newman, M.E.: Community Structure in Social and Biological Networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Newman, M.E.: Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)CrossRefGoogle Scholar
  4. 4.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community Structure in Time-Dependent, Multiscale, and Multiplex Networks. Science 328(5980), 876–878 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Mining Hidden Community in Heterogeneous Social Networks. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 58–65. ACM (2005)Google Scholar
  6. 6.
    Jaeger, M.: Relational Bayesian Networks. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, pp. 266–273. Morgan Kaufmann Publishers Inc. (1997)Google Scholar
  7. 7.
    Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-Organization and Identification of Web Communities. Computer 35(3), 66–70 (2002)CrossRefGoogle Scholar
  8. 8.
    Rattigan, M.J., Maier, M., Jensen, D.: Graph Clustering with Network Structure Indices. In: Proceedings of the 24th International Conference on Machine Learning, pp. 783–790. ACM (2007)Google Scholar
  9. 9.
    Ruan, J., Zhang, W.: An Efficient Spectral Algorithm for Network Community Discovery and its Applications to Biological and Social Networks. In: Seventh IEEE International Conference on Data Mining, pp. 643–648. IEEE (2007)Google Scholar
  10. 10.
    Fortunato, S.: Community Detection in Graphs. Physics Reports 486(3), 75–174 (2010)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Yang, B., Cheung, W.K., Liu, J.: Community Mining from Signed Social Networks. IEEE Transactions on Knowledge and Data Engineering 19(10), 1333–1348 (2007)CrossRefGoogle Scholar
  12. 12.
    Breiger, R.L., Boorman, S.A., Arabie, P.: An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling. Journal of Mathematical Psychology 12(3), 328–383 (1975)CrossRefGoogle Scholar
  13. 13.
    Taskar, B., Segal, E., Koller, D.: Probabilistic Classification and Clustering in Relational Data. In: International Joint Conference on Artificial Intelligence, vol. 17, pp. 870–878 (2001)Google Scholar
  14. 14.
    Kok, S., Domingos, P.: Statistical Predicate Invention. In: Proceedings of the 24th International Conference on Machine Learning, pp. 433–440. ACM (2007)Google Scholar
  15. 15.
    Xu, Z., Tresp, V., Yu, S., Yu, K.: Nonparametric Relational Learning for Social Network Analysis. In: KDD 2008 Workshop on Social Network Mining and Analysis (2008)Google Scholar
  16. 16.
    Jaeger, M.: Complex Probabilistic Modeling with Recursive Relational Bayesian Networks. Annals of Mathematics and Artificial Intelligence 32(1-4), 179–220 (2001)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Jaeger, M.: Parameter Learning for Relational Bayesian Networks. In: Proceedings of the 24th International Conference on Machine Learning. pp. 369–376. ACM (2007)Google Scholar
  18. 18.
    Zachary, W.W.: An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research 33(4), 452–473 (1977)Google Scholar
  19. 19.
    Newman, M.E., Girvan, M.: Finding and Evaluating Community Structure in Networks. Physical Review E 69(2), 026113 (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jiuchuan Jiang
    • 1
  • Manfred Jaeger
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityDenmark

Personalised recommendations