Abstract
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.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases (1994)
Altman, A., Tennenholtz, M.: Ranking systems: the pagerank axioms. In: ACM Conference on Electronic Commerce, pp. 1–8 (2005)
Barabasi, A.L.: Linked: The New Science of Networks. Perseus Books (2002)
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)
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)
Cheng, H., Yan, X., Han, J.: Mining graph patterns. In: Managing and Mining Graph Data, pp. 365–392 (2010)
Chua, F.C.T., Lauw, H.W., Lim, E.-P.: Predicting item adoption using social correlation. In: SDM, pp. 367–378 (2011)
Collard, M., Vansnick, J.: How to measure interestingness in data mining: a multiple criteria decision analysis approach. In: RCIS, pp. 395–400 (2007)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. 7, 3–12 (2005)
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)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 313–320 (2001)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov. 11, 243–271 (2005)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58, 1019–1031 (2007)
Nijssen, S., Kok, J.N.: The gaston tool for frequent subgraph mining. Electr. Notes Theor. Comput. Sci. 127(1), 77–87 (2005)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining (2002)
Yoon, S., Song, S., Kim, S.: Efficient link-based clustering in a large scaled blog network. In: Ubiquitous Information Management and Communication (2011)
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Stattner, E., Collard, M. (2012). FLMin: An Approach for Mining Frequent Links in Social Networks. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 294. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30567-2_38
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DOI: https://doi.org/10.1007/978-3-642-30567-2_38
Publisher Name: Springer, Berlin, Heidelberg
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