untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:37:44Z</responseDate>
<request identifier=oai:HAL:hal-00767052v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-00767052v1</identifier>
<datestamp>2017-12-21</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:BNRMI</setSpec>
<setSpec>collection:TDS-MACS</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>MAX-FLMin: An Approach for Mining Maximal Frequent Links and Generating Semantical Structures from 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>Database (Weston, Conn.) and Expert Systems Applications</source>
<source>23rd International Conference, DEXA 2012, Vienna, Austria, September 3-6, 2012. Proceedings, Part I</source>
<source>Database and Expert Systems Applications (DEXA)</source>
<coverage>Vienna, Austria</coverage>
<identifier>hal-00767052</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00767052</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00767052</source>
<source>Database and Expert Systems Applications (DEXA), 2012, Vienna, Austria. 7446, pp.468-483, 2012, 〈10.1007/978-3-642-32600-4_35〉</source>
<identifier>DOI : 10.1007/978-3-642-32600-4_35</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-32600-4_35</relation>
<language>en</language>
<subject>[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>The paper proposes a new knowledge discovery method called MAX-FLMin for extracting frequent patterns in social networks. Unlike traditional approaches that mainly focus on the network topological structure, the originality of our solution is its ability to exploit information both on the network structure and the attributes of nodes in order to elicit specific regularities that we call "Frequent Links". This kind of patterns provides relevant knowledge about the groups of nodes most connected within the network. First, we detail the method proposed to extract maximal frequent links from social networks. Second, we show how the extracted patterns are used to generate aggregated networks that represent the initial social network with more semantics. Qualitative and quantitative studies are conducted to evaluate the performances of our algorithm in various configurations.</description>
<date>2012</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
