FLMin: An Approach for Mining Frequent Links in Social Networks

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.
Type de document :
Communication dans un congrès
Springer. Networked Digital Technologies (NDT), 2012, Dubai, United Arab Emirates. 294, pp.449-463, 2012, 〈10.1007/978-3-642-30567-2_38〉
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https://hal.archives-ouvertes.fr/hal-00768431
Contributeur : Erick Stattner <>
Soumis le : vendredi 21 décembre 2012 - 14:26:53
Dernière modification le : lundi 21 mars 2016 - 17:28:33

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Erick Stattner, Martine Collard. FLMin: An Approach for Mining Frequent Links in Social Networks. Springer. Networked Digital Technologies (NDT), 2012, Dubai, United Arab Emirates. 294, pp.449-463, 2012, 〈10.1007/978-3-642-30567-2_38〉. 〈hal-00768431〉

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