untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:37:32Z</responseDate>
<request identifier=oai:HAL:hal-00768405v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-00768405v1</identifier>
<datestamp>2017-12-21</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>collection:BNRMI</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:TDS-MACS</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Towards Merging Models of Information Spreading and Dynamic Phenomena in Social Networks</title>
<creator>Stattner, Erick</creator>
<creator>Collard, Martine</creator>
<creator>Vidot, Nicolas</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>World Summit on the Knowledge Society</source>
<source>4TH World Summit on the Knowledge Society, Mykonos, Greece, 2011</source>
<source>World Summit on the Knowledge Society (WSKS)</source>
<coverage>Mykonos, Greece</coverage>
<contributor>Springer</contributor>
<identifier>hal-00768405</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00768405</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00768405</source>
<source>Springer. World Summit on the Knowledge Society (WSKS), 2011, Mykonos, Greece. pp.1-9, 2011</source>
<language>en</language>
<subject lang=en>Social networks</subject>
<subject lang=en>diffusion</subject>
<subject lang=en>dynamics</subject>
<subject lang=en>modelisation</subject>
<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>While the impact of network properties on information spread- ing is now widely studied, in uence of network dynamics is very little known. In this paper, we study how evolution mechanisms tradition- ally observed within social networks can a ect information di usion. We present an approach that merges two models: model of informa- tion di usion through social networks and model of network evolution. Since epidemics provide a reference in application domains of information spreading, we measure the impact of basic network structure changes on epidemic peak value and timing. Then we investigate observed trends in terms of changes appearing in the network structure. Our results provide promising results on how and why network dynamics is a strong param- eter to integrate in requirements for information spreading modelling.</description>
<date>2011</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
