FLMin: An Approach for Mining Frequent Links in Social Networks

  • Erick Stattner
  • Martine Collard
Part of the Communications in Computer and Information Science book series (CCIS, volume 294)


This paper proposes a new knowledge discovery method called FLMin to discover frequent patterns in a social network. The algorithm works without previous knowledge on the network and exploits both the structure and the attributes of nodes to extract regularities called Frequent Links. Unlike traditional works in this area that solely exploit structural regularities of the network, the originality of FLMin is its ability to gather these two kinds of information in the search for patterns. In this paper, we detail the method proposed for extracting frequent links and discuss its complexity and its flexibility. The efficiency of our solution is evaluated by conducting qualitative and quantitative studies for understanding how behaves FLMin according to different parameters.


Social Network Network Size Frequent Pattern Link Prediction Undirected Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases (1994)Google Scholar
  2. 2.
    Altman, A., Tennenholtz, M.: Ranking systems: the pagerank axioms. In: ACM Conference on Electronic Commerce, pp. 1–8 (2005)Google Scholar
  3. 3.
    Barabasi, A.L.: Linked: The New Science of Networks. Perseus Books (2002)Google Scholar
  4. 4.
    Barrett, C.L., Bisset, K.R., Eubank, S.G., Feng, X., Marathe, M.V.: Episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In: Conference on Supercomputing, pp. 1–12 (2008)Google Scholar
  5. 5.
    Brisson, L., Collard, M.: How to Semantically Enhance a Data Mining Process? In: Filipe, J., Cordeiro, J. (eds.) ICEIS 2008. LNBIP, vol. 19, pp. 103–116. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Cheng, H., Yan, X., Han, J.: Mining graph patterns. In: Managing and Mining Graph Data, pp. 365–392 (2010)Google Scholar
  7. 7.
    Chua, F.C.T., Lauw, H.W., Lim, E.-P.: Predicting item adoption using social correlation. In: SDM, pp. 367–378 (2011)Google Scholar
  8. 8.
    Collard, M., Vansnick, J.: How to measure interestingness in data mining: a multiple criteria decision analysis approach. In: RCIS, pp. 395–400 (2007)Google Scholar
  9. 9.
    Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. 7, 3–12 (2005)CrossRefGoogle Scholar
  10. 10.
    Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 313–320 (2001)Google Scholar
  12. 12.
    Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov. 11, 243–271 (2005)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58, 1019–1031 (2007)CrossRefGoogle Scholar
  14. 14.
    Nijssen, S., Kok, J.N.: The gaston tool for frequent subgraph mining. Electr. Notes Theor. Comput. Sci. 127(1), 77–87 (2005)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining (2002)Google Scholar
  16. 16.
    Yoon, S., Song, S., Kim, S.: Efficient link-based clustering in a large scaled blog network. In: Ubiquitous Information Management and Communication (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Erick Stattner
    • 1
  • Martine Collard
    • 1
  1. 1.LAMIA LaboratoryUniversity of the French West Indies and GuianaFrance

Personalised recommendations