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<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd> <responseDate>2018-01-15T18:33:20Z</responseDate> <request identifier=oai:HAL:hal-00874344v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request> <GetRecord> <record> <header> <identifier>oai:HAL:hal-00874344v1</identifier> <datestamp>2018-01-12</datestamp> <setSpec>type:COMM</setSpec> <setSpec>subject:info</setSpec> <setSpec>collection:CNRS</setSpec> <setSpec>collection:I3S</setSpec> <setSpec>collection:BNRMI</setSpec> <setSpec>collection:UNIV-AG</setSpec> <setSpec>collection:UNICE</setSpec> <setSpec>collection:TDS-MACS</setSpec> <setSpec>collection:UCA-TEST</setSpec> <setSpec>collection:UNIV-COTEDAZUR</setSpec> </header> <metadata><dc> <publisher>HAL CCSD</publisher> <title lang=en>Simulating human mobility and information diffusion</title> <creator>Collard, Martine</creator> <creator>Collard, Philippe</creator> <creator>Stattner, Erick</creator> <contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor> <contributor>Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Groupe SCOBI ; Modèles Discrets pour les Systèmes Complexes (MDSC) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor> <description>International audience</description> <source>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source> <source>2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)</source> <source>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining</source> <coverage>Niagara falls, Canada</coverage> <contributor>IEEE/ACM</contributor> <identifier>hal-00874344</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-00874344</identifier> <source>https://hal.archives-ouvertes.fr/hal-00874344</source> <source>IEEE/ACM. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013, Niagara falls, Canada. pp.1-5, 2013</source> <language>en</language> <subject lang=en>Social network</subject> <subject lang=en>mobility</subject> <subject lang=en>multi-agent systems</subject> <subject>[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation</subject> <subject>[INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA]</subject> <type>info:eu-repo/semantics/conferenceObject</type> <type>Conference papers</type> <description lang=en>Human spatial motions determine geographic social contacts that influence the way an information is spread on a population or a community. As mobility is a transverse dimension to social practices it is important to better understand its role. With the Eternal-Return model we propose, we simulate an artificial world populated by heterogeneous agents who differ in their mobility. We have chosen a multi-agent framework perspective for this simulation. We endow the agents with simple rules on how to move around the space and how to establish proximity-contacts. This allows to distinguish different kinds of mobile agents, from sedentary ones to travelers. To summarize the dynamics induced by mobility over time, we define the mobility-based Social Proximity Network as being the network of all distinct contacts between agents. Its properties give insight in the process of information spreading. We conduct simulations to understand how an information can be broadcast when agent-nodes are in motion.</description> <date>2013</date> </dc> </metadata> </record> </GetRecord> </OAI-PMH>