An Algorithm for Partitioning Community Graph into Sub-community Graphs Using Graph Mining Techniques

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 44)


Using graph mining techniques, knowledge extraction is possible from the community graph. In our work, we started with the discussion on related definitions of graph partition both mathematical as well as computational aspects. The derived knowledge can be extracted from a particular sub-graph by way of partitioning a large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The partition is aiming at the edges among the community members of different communities. We have initiated our work by studying techniques followed by different researchers, thus proposing a new and simple algorithm for partitioning the community graph in a social network using graph techniques. An example verifies about the strength and easiness of the proposed algorithm.


Adjacency matrix Cluster Community Graph partition Sub-Graph 


  1. 1.
    Rajaraman, A., Leskovec, J., Ullman, J.D.: Mining of Massive Datasets. Copyright © 2010, 2011, 2012, 2013, 2014Google Scholar
  2. 2.
    Mitra, A., Satpathy, S.R., Paul, S.: Clustering analysis in social network using covering based rough set. In: 2013 IEEE 3rd International Advance Computing Conference (IACC), India, 22 Feb 2013, pp. 476–481, 2013Google Scholar
  3. 3.
    Andrews, G.E.: The Theory of Partitions. Addison-Wesley, Boston, USA (1976)MATHGoogle Scholar
  4. 4.
    Lovasz, L.: Combinatorial Problems and Exercises. North-Holland, Amsterdam, The etherlands (1993)MATHGoogle Scholar
  5. 5.
    Ravasz, E., Barabasi, A.L.: Phys. Rev. E 67(2), 026112 (2003)CrossRefGoogle Scholar
  6. 6.
    Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Science 297(5586), 1551 (2002)CrossRefGoogle Scholar
  7. 7.
    Kernighan, B.W., Lin, S.: Bell Syst. Tech. J. 49, 291 (1970)CrossRefMATHGoogle Scholar
  8. 8.
    Suaris, P.R., Kedem, G.: IEEE Trans. Circuits Syst. 35, 294 (1988)CrossRefGoogle Scholar
  9. 9.
    Barnes, E.R.: SIAM J. Alg. Discr. Meth. 3, 541 (1982)Google Scholar
  10. 10.
    Scholtz, R.A.: The spread spectrum concept. In: Abramson, N. (ed) Multiple Access, Piscataway, NJ: IEEE Press, ch. 3, pp. 121–123 (1993)Google Scholar
  11. 11.
    Ford, L.R., Fulkerson, D.R.: Canadian J. Math. 8, 399 (1956)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Goldberg, A.V., Tarjan, R.E.: J. ACM 35, 921 (1988)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Flake, G.W., Lawrence, S., Giles, C.L.: In: Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM Press, Boston, USA), pp. 150–160 (2000)Google Scholar
  14. 14.
    Flake, G.W., Lawrence, S., Lee Giles, C., Coetzee, F.M.: IEEE Comput. 35, 66 (2002)Google Scholar
  15. 15.
    Pothen, A.: Graph Partitioning Algorithms with Applications to Scientific Computing. Technical Report, Norfolk, VA, USA (1997)CrossRefGoogle Scholar
  16. 16.
    Rao, B., Mitra, A.: A new approach for detection of common communities in a social network using graph mining techniques. In: 2014 International Conference on High Performance Computing and Applications (ICHPCA), pp. 1–6, 22–24 Dec 2014. doi:  10.1109/ICHPCA.2014.7045335
  17. 17.
    Rao, B., Mitra, A.: An approach to merging of two community sub-graphs to form a community graph using graph mining techniques. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC-2014), 978-1-4799-3972-5/14/$31.00 @2014, pp. 460–466, Coimbatore, India, Dec 2014Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of CSE and ITV.I.T.A.M.BerhampurIndia

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