Discovering Communities in Heterogeneous Social Networks Based on Non-negative Tensor Factorization and Cluster Ensemble Approach

  • Ankita Verma
  • K. K. Bharadwaj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)


Identification of the appropriate community structure in social networks is an arduous task. The intricacy of the problem increases with the heterogeneity of multiple types of objects and relationships involved in the analysis of the network. Traditional approaches for community detection focus on the networks comprising of content features and linkage information of the set of single type of entities. However, rich social media networks are usually heterogeneous in nature with multiple types of relationships existing between different types of entities. Cognizant to these requirements, we develop a model for community detection in Heterogeneous Social Networks (HSNs) employing non-negative tensor factorization method and cluster ensemble approach. Extensive experiments are performed on 20Newsgroup dataset which establish the effectiveness and efficiency of our scheme.


Heterogeneous social networks (HSNs) Community detection Social network analysis Non-negative tensor factorization (NTF) Cluster ensemble 


  1. 1.
    Anand, D., Bharadwaj, K.K.: Pruning trust–distrust network via reliability and risk estimates for quality recommendations. Soc. Netw. Anal. Min. 3(1), 65–84 (2012)CrossRefGoogle Scholar
  2. 2.
    Bharadwaj, K.K., Al-Shamri, M.Y.H.: Fuzzy computational models for trust and reputation systems. Electron. Commer. Res. Appl. 8(1), 37–47 (2009)CrossRefGoogle Scholar
  3. 3.
    Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Mining hidden community in heterogeneous social networks. In: Proceedings of 3rd International Workshop on Link discovery, pp. 58–65 (2005)Google Scholar
  4. 4.
    Chen, Y., Wang, L., Dong, M.: Non-negative matrix factorization for semi-supervised heterogeneous data co-clustering. IEEE Trans. Knowl. Data Eng. 22(10), 1459–1474 (2010)CrossRefGoogle Scholar
  5. 5.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of 7th ACM SIGKDD International Conference Knowledge Discovery Data Mining, pp. 269–274 (2001)Google Scholar
  6. 6.
    Franz, T., Schultz, A., Sizov, S., Staab, S.: TripleRank: ranking semantic web data by tensor decomposition. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., M, E., T, Krishnaprasad (eds.) ISWC 2009. LNCS, vol. 5823, pp. 213–228. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Gao, B., Liu, T.Y., Zheng, X., Cheng, Q.S., Ma, W.Y.: Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering. In: Proceedings of ACM KDD, pp. 41–50 (2005)Google Scholar
  8. 8.
    Getoor, L.: An introduction to probabilistic graphical models for relational data. IEEE Data Eng. Bull. 29(1), 32–39 (2006)MathSciNetGoogle Scholar
  9. 9.
    Goder, A., Filkov, V.: Consensus clustering algorithms: comparison and refinement. In: Proceedings of 10th Workshop on Algorithm Engineering and Experiments, pp. 109–117 (2008)Google Scholar
  10. 10.
    Holder, L.B., Cook, D.J.: Graph-based relational learning: current and future directions. ACM SIGKDD Explorations Newsletter 5(1), 90–93 (2003)CrossRefGoogle Scholar
  11. 11.
    Lang, K.: News weeder: learning to filter netnews. In: Proceedings of International Conference on Machine Learning, pp. 331–339 (1995)Google Scholar
  12. 12.
    Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data 3(2), 1–31 (2009)CrossRefGoogle Scholar
  13. 13.
    Long, B., Philip, S.Y., Zhang, Z.: A general model for multiple view unsupervised learning. In: Proceedings of SIAM International Conference on Data Mining, pp. 822–833 (2008)Google Scholar
  14. 14.
    Long, B., Zhang, Z.M., Wu, X., Yu, P.S.: Spectral clustering for multi-type relational data. In: Proceedings of 23rd International Conference on Machine learning, pp. 585–592 (2006)Google Scholar
  15. 15.
    Long, B., Zhang, Z.M., Yu, P.S.: A probabilistic framework for relational clustering. In: Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 470–479 (2007)Google Scholar
  16. 16.
    Mei, J.P., Chen, L.: A fuzzy approach for multi-type relational data clustering. IEEE Trans. Fuzzy Syst. 20(2), 358–371 (2012)CrossRefGoogle Scholar
  17. 17.
    Nguyen, N., Caruana, R.: Consensus clusterings. In: Proceedings of 7th IEEE International Conference on Data Mining, pp. 607–612 (2007)Google Scholar
  18. 18.
    Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: Proceedings of 28th International Conference on Machine Learning, pp. 809–816 (2011)Google Scholar
  19. 19.
    Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)zbMATHMathSciNetGoogle Scholar
  20. 20.
    Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. In: Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 677–685 (2008)Google Scholar
  21. 21.
    Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Disc. 25(1), 1–33 (2011)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wang, J., Zeng, H., Chen, Z., Lu, H., Tao, L., Ma, W.Y.: ReCoM: reinforcement clustering of multi-type interrelated data objects. In: Proceedings of 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 274–281 (2003)Google Scholar
  23. 23.
    Wu, Z., Yin, W., Cao, J., Xu, G., Cuzzocrea, A.: Community detection in multi-relational social networks. In: Proceedings of International Conference on Web Information Systems Engineering, pp. 43–56 (2013)Google Scholar
  24. 24.
    Zhou, D., Burges, C.J.: Spectral clustering and transductive learning with multiple views. In: Proceedings of 24th International Conference on Machine learning, pp. 1159–1166 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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