untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:36:29Z</responseDate>
<request identifier=oai:HAL:hal-00786161v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-00786161v1</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>Social-Based Conceptual Links: Conceptual Analysis Applied to 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>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)</source>
<source>2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source>
<source>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source>
<coverage>Istanbul, Turkey</coverage>
<contributor>IEEE/ACM</contributor>
<identifier>hal-00786161</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00786161</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00786161</source>
<source>IEEE/ACM. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012, Istanbul, Turkey. pp.25-29, 2012, 〈10.1109/ASONAM.2012.15〉</source>
<identifier>DOI : 10.1109/ASONAM.2012.15</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/ASONAM.2012.15</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>In this work, we propose a novel approach for the discovery of frequent patterns in a social network on the basis of both vertex attributes and link frequency. With an analogy to the traditional task of mining frequent item sets, we show that the issue addressed can be formulated in terms of a conceptual analysis that elicits conceptual links. A social-based conceptual link is a synthetic representation of a set of links between groups of vertexes that share similar internal properties. We propose a first algorithm that optimizes the search into the concept lattice of conceptual links and extracts maximal frequent conceptual links. We study the performances of our solution and give experimental results obtained on a sample example. Finally we show that the set of conceptual links extracted provides a conceptual view of the social network.</description>
<date>2012</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
