Addressing Object Heterogeneity Through Edge Cluster in Multi-mode Networks

  • Shashikumar G. Totad
  • A. Smitha Kranthi
  • A. K. Matta
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


The oceans of data generated by social media have become a goldmine to researchers in the data mining domain. Discovering actionable knowledge by extracting latent patterns has many advantages. One of the utilities of mining social data is learning collective behavior which helps in taking well informed decisions pertaining to humanitarian assistance, disaster relief and such real world applications. In multi mode while studying the collective behavior using edge centric approach, object heterogeneity is a problem. In this paper, we propose a scheme temporal regularized evolutionary multimode clustering algorithm which can address object heterogeneity in social media with multi-mode more effectively. With this the prediction performance of collective behavior is improved further. We built a prototype application to demonstrate the proof of perception. The empirical results are encouraging and our approach can be used in real world applications that mine social media data explicitly.


Social networking Data mining Social dimensions Collective behavior 


  1. 1.
    Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)MATHGoogle Scholar
  2. 2.
    Macskassy, S.A., Provost, F.: A simple relational classifier. In: Proceedings of Multi-Relational Data Mining Workshop (MRDM) at the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)Google Scholar
  3. 3.
    Xu, Z., Tresp, V., Yu, S., Yu, K.: Nonparametric relational learning for social network analysis. In: KDD’08: Proceedings of Workshop Social Network Mining and Analysis (2008)Google Scholar
  4. 4.
    Neville, J., Jensen, D.: Leveraging relational auto correlation with latent group models. In: MRDM’05: Proceedings of Fourth International Workshop Multi-Relational Mining, pp. 49–55 (2005)Google Scholar
  5. 5.
    Tang, L., Liu, H.: Relational learning via latent social dimensions. In: KDD’09: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 817–826 (2009)Google Scholar
  6. 6.
    Newman, M.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E (Stat. Nonlin. Soft Matter Phys). 74(3), 036104. (2006)
  7. 7.
    Luxburg, U.V.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Airodi, E., Blei, D., Fienberg, S., Xing, E.P.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)Google Scholar
  9. 9.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)Google Scholar
  10. 10.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)Google Scholar
  11. 11.
    Shen, H., Cheng, X., Cai, K., Hu, M.: Detect overlapping and hierarchical community structure in networks. Phys. A: Stat. Mech. Appl. 388(8), 1706–1712 (2009)Google Scholar
  12. 12.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113. 2004. 1090 IEEE Trans. Know. Data Eng. 24(6) (2012)
  13. 13.
    Gregory, S.: An algorithm to find overlapping community structure in networks. In: Proceedings of European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 91–102. (2007)
  14. 14.
    Evans, T., Lambiotte, R.: Line graphs, link partitions and overlapping communities. Phys. Rev. E 80(1), 16105 (2009)CrossRefGoogle Scholar
  15. 15.
    Ahn, Y.Y., Bagrow, J.P., Lehmann S.: Link communities reveal multi-scale complexity in networks. (2009)
  16. 16.
    Tang, L., Wang, X., Liu, H.: Scalable learning of collective behavior. IEEE Trans. Knowl. Data Eng. 24(6) (2012)Google Scholar
  17. 17.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatkom, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRefGoogle Scholar
  18. 18.
    Bentley, J.: Multidimensional binary search trees used for associative searching. Comm. ACM 18, 509–175 (1975)Google Scholar
  19. 19.
    Jin, R., Goswami, A.Y., Agrawal, G.: Fast and exact out-of-core and distributed k-means clustering. Knowl. Inf. Syst. 10(1), 17–40 (2006)CrossRefGoogle Scholar
  20. 20.
    Bradley, P., Fayyad, U., Reina, C.: Scaling clustering algorithms to large databases. In: Proceedings of ACM Knowledge Discovery and Data Mining (KDD) Conference (1998)Google Scholar
  21. 21.
    Sato, M., Shii, S.: On-line EM algorithm for the normalized Gaussian network. Neural Comput. 12, 407–432 (2000)CrossRefGoogle Scholar
  22. 22.
    Wasserman, S., Faust, K.: Social Network Analysis Methods and Applications. Cambridge University Press, Cambridge (1994)Google Scholar
  23. 23.
    Baumes, J., Goldberg, M., Ismail, M.M., Wallace, W.: Discovering hidden groups in communication networks. In: 2nd NSF/NIJ Symposium on Intelligence and Security Informatics (2004)Google Scholar
  24. 24.
    Meyers, M.N.L.A., Pourbohloul, B.: Predicting epidemics on directed contact networks. J. Theor. Biol. 240, 400–418 (2006)Google Scholar
  25. 25.
    Tang, L., Liu, H.: Toward predicting collective behavior via social dimension extraction. IEEE Intell. Syst. 25(4), 19–25 (2010)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Tang, L., Liu, H., Zhang, J., Nazeri, Z.: Community evolution in dynamic multi-mode networks. KDD’08 (2008)Google Scholar
  27. 27.
    Yu, K., Yu, S., Tresp, V.: Soft clustering on graphs. In: Proceedings of Advances in Neural Information Processing Systems (NIPS) (2005)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Shashikumar G. Totad
    • 1
  • A. Smitha Kranthi
    • 1
  • A. K. Matta
    • 2
  1. 1.Department of Computer Science and EngineeringGMRITRazamIndia
  2. 2.Department of Mechanical EngineeringGMRITRazamIndia

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