Diffusion in Dynamic Social Networks: Application in Epidemiology
Identifieur interne : 001660 ( Istex/Corpus ); précédent : 001659; suivant : 001661Diffusion in Dynamic Social Networks: Application in Epidemiology
Auteurs : Erick Stattner ; Martine Collard ; Nicolas VidotSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: 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.
Url:
DOI: 10.1007/978-3-642-23091-2_49
Links to Exploration step
ISTEX:821BA55B3AA26FF8B09F4505AD9175B5D3E587F1Le document en format XML
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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. 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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. 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key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation><orgName type="institution">ETH Zurich</orgName><address><settlement>Zurich</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation><orgName type="institution">Stanford University</orgName><address><settlement>Stanford</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation><orgName type="institution">Weizmann Institute of Science</orgName><address><settlement>Rehovot</settlement><country key="IL">ISRAEL</country></address></affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation><orgName type="institution">University of Bern</orgName><address><settlement>Bern</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation><orgName type="institution">Indian Institute of Technology</orgName><address><settlement>Madras</settlement><country key="IN">INDIA</country></address></affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation><orgName type="institution">University of Dortmund</orgName><address><settlement>Dortmund</settlement><country key="DE">GERMANY</country></address></affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation><orgName type="institution">Massachusetts Institute of 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key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>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 <hi rend="italic">MIDEN</hi> framework, an approach to measure impact of basic changes in network structure, and <hi rend="italic">DynSpread</hi>, a 2D simulation tool designed to replay infections scenarios on evolving networks.</p></abstract><textClass ana="keyword"><keywords xml:lang="en"><term>Information Spreading</term><term>Dynamic network</term><term>Evolution</term><term>Framework</term><term>Simulation</term></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I18040</label><item><term type="Secondary" subtype="priority-1">Information Systems Applications (incl.Internet)</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-2">Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-23091-2</BookID><BookTitle>Database and Expert Systems Applications</BookTitle><BookSubTitle>22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, 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Robotics)</BookSubject><BookSubject Code="I13022" Priority="3" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I18032" Priority="4" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18024" Priority="5" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18030" Priority="6" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Abdelkader</GivenName><FamilyName>Hameurlain</FamilyName></EditorName><Contact><Email>hameur@irit.fr</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Stephen</GivenName><GivenName>W.</GivenName><FamilyName>Liddle</FamilyName></EditorName><Contact><Email>liddle@byu.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Klaus-Dieter</GivenName><FamilyName>Schewe</FamilyName></EditorName><Contact><Email>kd.schewe@scch.at</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Xiaofang</GivenName><FamilyName>Zhou</FamilyName></EditorName><Contact><Email>zxf@uq.edu.au</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>IRIT Institut de Recherche en Informatique de Toulouse</OrgDivision><OrgName>Paul Sabatier University</OrgName><OrgAddress><Street>118, route de Narbonne</Street><Postcode>31062</Postcode><City>Toulouse Cedex</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Brigham Young University, 784 TNRB</OrgName><OrgAddress><Postcode>84602</Postcode><City>Provo</City><State>UT</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>Software Competence Center Hagenberg and Johannes-Keppler-University Linz</OrgName><OrgAddress><Street>Softwarepark 21</Street><Postcode>4232</Postcode><City>Hagenberg</City><Country>Austria</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>School of Information Technology and Electrical Engineering</OrgDivision><OrgName>University of Queensland</OrgName><OrgAddress><Postcode>QLD 4072</Postcode><City>Brisbane</City><Country>Australia</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part13"><PartInfo TocLevels="0"><PartID>13</PartID><PartSequenceNumber>13</PartSequenceNumber><PartTitle>Distribution</PartTitle><PartChapterCount>3</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Database and Expert Systems Applications</BookTitle></PartContext></PartInfo><Chapter ID="Chap49" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>49</ChapterID><ChapterDOI>10.1007/978-3-642-23091-2_49</ChapterDOI><ChapterSequenceNumber>49</ChapterSequenceNumber><ChapterTitle Language="En">Diffusion in Dynamic Social Networks: Application in Epidemiology</ChapterTitle><ChapterFirstPage>559</ChapterFirstPage><ChapterLastPage>573</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>13</PartID><BookID>978-3-642-23091-2</BookID><BookTitle>Database and Expert Systems Applications</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Erick</GivenName><FamilyName>Stattner</FamilyName></AuthorName><Contact><Email>estattne@univ-ag.fr</Email><URL>http://lamia.univ-ag.fr</URL></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Martine</GivenName><FamilyName>Collard</FamilyName></AuthorName><Contact><Email>mcollard@univ-ag.fr</Email><URL>http://lamia.univ-ag.fr</URL></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Nicolas</GivenName><FamilyName>Vidot</FamilyName></AuthorName><Contact><Email>nvidot@univ-ag.fr</Email><URL>http://lamia.univ-ag.fr</URL></Contact></Author><Affiliation ID="Aff20"><OrgDivision>LAMIA Laboratory</OrgDivision><OrgName>University of the French West Indies and Guiana</OrgName><OrgAddress><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>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 <Emphasis Type="Italic">MIDEN</Emphasis> framework, an approach to measure impact of basic changes in network structure, and <Emphasis Type="Italic">DynSpread</Emphasis>, a 2D simulation tool designed to replay infections scenarios on evolving networks.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Information Spreading</Keyword><Keyword>Dynamic network</Keyword><Keyword>Evolution</Keyword><Keyword>Framework</Keyword><Keyword>Simulation</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Diffusion in Dynamic Social Networks: Application in Epidemiology</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Diffusion in Dynamic Social Networks: Application in Epidemiology</title></titleInfo><name type="personal"><namePart type="given">Erick</namePart><namePart type="family">Stattner</namePart><affiliation>LAMIA Laboratory, University of the French West Indies and Guiana, France</affiliation><affiliation>E-mail: estattne@univ-ag.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Martine</namePart><namePart type="family">Collard</namePart><affiliation>LAMIA Laboratory, University of the French West Indies and Guiana, France</affiliation><affiliation>E-mail: mcollard@univ-ag.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nicolas</namePart><namePart type="family">Vidot</namePart><affiliation>LAMIA Laboratory, University of the French West Indies and Guiana, France</affiliation><affiliation>E-mail: nvidot@univ-ag.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: 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.</abstract><subject lang="en"><genre>Keywords</genre><topic>Information Spreading</topic><topic>Dynamic network</topic><topic>Evolution</topic><topic>Framework</topic><topic>Simulation</topic></subject><relatedItem type="host"><titleInfo><title>Database and Expert Systems Applications</title><subTitle>22nd International Conference, DEXA 2011, Toulouse, France, August 29 - September 2, 2011, Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">Abdelkader</namePart><namePart type="family">Hameurlain</namePart><affiliation>IRIT Institut de Recherche en Informatique de Toulouse, Paul Sabatier University, 118, route de Narbonne, 31062, Toulouse Cedex, France</affiliation><affiliation>E-mail: hameur@irit.fr</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Stephen</namePart><namePart type="given">W.</namePart><namePart type="family">Liddle</namePart><affiliation>Brigham Young University, 784 TNRB, 84602, Provo, UT, USA</affiliation><affiliation>E-mail: liddle@byu.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Klaus-Dieter</namePart><namePart type="family">Schewe</namePart><affiliation>Software Competence Center Hagenberg and Johannes-Keppler-University Linz, Softwarepark 21, 4232, Hagenberg, Austria</affiliation><affiliation>E-mail: kd.schewe@scch.at</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Xiaofang</namePart><namePart type="family">Zhou</namePart><affiliation>School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Brisbane, Australia</affiliation><affiliation>E-mail: zxf@uq.edu.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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