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<identifier>oai:HAL:hal-00768431v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>FLMin: An Approach for Mining Frequent Links in Social Networks</title>
<creator>Stattner, Erick</creator>
<creator>Collard, Martine</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Networked Digital Technologies</source>
<source>4th International Conference, NDT 2012, Dubai, UAE, April 24-26, 2012, Proceedings, Part II</source>
<source>Networked Digital Technologies (NDT)</source>
<coverage>Dubai, United Arab Emirates</coverage>
<contributor>Springer</contributor>
<identifier>hal-00768431</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00768431</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00768431</source>
<source>Springer. Networked Digital Technologies (NDT), 2012, Dubai, United Arab Emirates. 294, pp.449-463, 2012, 〈10.1007/978-3-642-30567-2_38〉</source>
<identifier>DOI : 10.1007/978-3-642-30567-2_38</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-30567-2_38</relation>
<language>en</language>
<subject lang=en>Social networks</subject>
<subject lang=en>social network mining</subject>
<subject lang=en>link mining</subject>
<subject lang=en>frequent links</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation</subject>
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<type>Conference papers</type>
<description lang=en>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.</description>
<date>2012</date>
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