Community Detection in Online Social Network Using Graph Embedding and Hierarchical Clustering

  • Vang LeEmail author
  • Vaclav Snasel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)


The community detection plays an important role in social network analysis. It can be used to find users that behave in a similar manner, detect groups of interests, cluster users in e-commerce application such as their taste or shopping habits, etc. In this paper, we proposed an algorithm to detect the community in online social networks. Our algorithm represents the nodes and the relationships in the social networks using a vector, agglomerative clustering (the most famous clustering algorithm) will cluster those vectors to figure out the communities. The experimental results show that our algorithm performs better traditional agglomerative clustering because of the ability to detect the community which has better modularity value.


Community detection Graph embedding Hierarchical clustering Social network analysis 


  1. 1.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Hofmann, T., Buhmann, J.M.: Multidimensional scaling and data clustering. In: NIPS, pp. 459–466 (1994)Google Scholar
  3. 3.
    Chen, M., Tsang, I.W., Tan, M., Jen, C.T.: A unified feature selection framework for graph embedding on high dimensional data. IEEE Trans. Knowl. Data Eng. 27(6), 1465–1477 (2015)CrossRefGoogle Scholar
  4. 4.
    Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRefGoogle Scholar
  5. 5.
    Zhou, C., Liu, Y., Liu, X., Liu, Z., Gao, J.: Scalable graph embedding for asymmetric proximity. In: AAAI, pp. 2942–2948 (2017)Google Scholar
  6. 6.
    Feng, J., Huang, M., Yang, Y., Zhu, X.: GAKE: graph aware knowledge embedding. In: COLING, pp. 641–651 (2016)Google Scholar
  7. 7.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)Google Scholar
  8. 8.
    Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types. In: IJCAI, pp. 2965–2971 (2016)Google Scholar
  9. 9.
    Alharbi, B., Zhang, X.: Learning from your network of friends: a trajectory representation learning model based on online social ties. In: ICDM, pp. 781–786 (2016)Google Scholar
  10. 10.
    Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: KDD, pp. 701–710 (2014)Google Scholar
  11. 11.
    Yang, Z., Tang, J., Cohen, W.: Multi-modal Bayesian embeddings for learning social knowledge graphs. In: IJCAI, pp. 2287–2293 (2016)Google Scholar
  12. 12.
    Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: ACL (2016)Google Scholar
  13. 13.
    Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: IJCAI, pp. 1895–1901 (2016)Google Scholar
  14. 14.
    Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: KDD, pp. 135–144 (2017)Google Scholar
  15. 15.
    Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: KDD, pp. 1225–1234 (2016)Google Scholar
  16. 16.
    Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014–2023, 11 June 2016Google Scholar
  17. 17.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)CrossRefGoogle Scholar
  19. 19.
    Barnes, E.R.: An algorithm for partitioning the nodes of a graph. SIAM J. Algebr. Discret. Methods 3(4), 541–550 (1982)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Shahriar, F., Nesar, A.B., Mahbub, N.M., Shatabda, S.: EGAGP: an enhanced genetic algorithm for producing efficient graph partitions. In: 2017 4th International Conference on Networking, Systems and Security (NSysS), pp. 1–9. IEEE, 18 December 2017Google Scholar
  21. 21.
    Reijnders, B.J.: Pre-processing Road Networks for Graph Partitioning Using Edge-Betweenness Centrality. Bachelor’s thesisGoogle Scholar
  22. 22.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, no. 14, pp. 281–297, 21 June 1967Google Scholar
  23. 23.
    Hlaoui, A., Wang, S.: A direct approach to graph clustering. Neural Netw. Comput. Intell. 4(8), 158–163 (2004)Google Scholar
  24. 24.
    Bezdek, J.C.: Objective Function Clustering. In: Pattern Recognition with Fuzzy Objective Function Algorithms, pp. 43–93. Springer, Boston (1981)CrossRefGoogle Scholar
  25. 25.
    Donath, W.E., Hoffman, A.J.: Lower bounds for the partitioning of graphs. IBM J. Res. Dev. 17(5), 420–425 (1973)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Li, L., Wang, S., Xu, S., Yang, Y.: Constrained spectral clustering using nyström method. Procedia Comput. Sci. 31(129), 9–15 (2018)CrossRefGoogle Scholar
  27. 27.
    Bhattacharyya, S., Chatterjee, S.: Spectral clustering for multiple sparse networks: I. arXiv preprint arXiv:1805.10594, 27 May 2018
  28. 28.
    Li, X., Kao, B., Luo, S., Ester, M.: ROSC: robust spectral clustering on multi-scale data. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 157–166. International World Wide Web Conferences Steering Committee, 10 April 2018Google Scholar
  29. 29.
    Sharma, A., López, Y., Tsunoda, T.: Divisive hierarchical maximum likelihood clustering. BMC Bioinform. 18(16), 546 (2017)CrossRefGoogle Scholar
  30. 30.
    Murata, T.: Detecting communities in social networks. In: Handbook of Social Network Technologies and Applications, pp. 269–280. Springer, Boston (2010)CrossRefGoogle Scholar
  31. 31.
    Madani, F.: ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis. Scientometrics 105(1), 323–335 (2015)CrossRefGoogle Scholar
  32. 32.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM, 13 August 2016Google Scholar
  33. 33.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, vol. 1. Np. Springer, New York (2001)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Department of Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

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