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<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Diffusion in Dynamic Social Networks: Application in Epidemiology</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>Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, Proceedings, Part II</source>
<source>Database and Expert Systems Applications - 22nd International Conference, DEXA 2011</source>
<coverage>Toulouse, France</coverage>
<contributor>Springer</contributor>
<identifier>hal-00614925</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00614925</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00614925</source>
<source>Springer. Database and Expert Systems Applications - 22nd International Conference, DEXA 2011, 2011, Toulouse, France. 6861 (2), pp.559-573, 2011, Lecture Notes in Computer Science. 〈10.1007/978-3-642-23091-2_49〉</source>
<identifier>DOI : 10.1007/978-3-642-23091-2_49</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-23091-2_49</relation>
<language>en</language>
<subject lang=en>Information Spreading</subject>
<subject lang=en>Dynamic network</subject>
<subject lang=en>Evolution</subject>
<subject lang=en>Framework</subject>
<subject lang=en>Simulation</subject>
<subject>[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation</subject>
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<type>Conference papers</type>
<description lang=en>Structure and evolution of networks have been areas of growing interest in recent years, especially with the emergence of Social Network Analysis (SNA) and its application in numerous fields. Researches on diffusion are focusing on network modeling for studying spreading phenomena. While the impact of network properties on spreading is now widely studied, involvement of network dynamicity is very little known. In this paper, we address the epidemiology context and study the consequences of network evolutions on spread of diseases. Experiments are conducted by comparing incidence curves obtained by evolution strategies applied on two generated and two real networks. Results are then analyzed by investigating network properties and discussed in order to explain how network evolution influences the spread. We present the MIDEN framework, an approach to measure impact of basic changes in network structure, and DynSpread, a 2D simulation tool designed to replay infections scenarios on evolving networks.</description>
<date>2011</date>
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