Detecting Leaders in Behavioral networks
Identifieur interne : 002B40 ( Main/Exploration ); précédent : 002B39; suivant : 002B41Detecting Leaders in Behavioral networks
Auteurs : Ilham Esslimani [France] ; Armelle Brun [France] ; Anne Boyer [France]Source :
English descriptors
Abstract
The development of the Web engendered the emergence of virtual communities. Analyzing information flows and discovering leaders through these communities becomes thus, a major challenge in different application areas. In this paper, we present an algorithm that aims at detecting leaders in the context of behavioral networks. This algorithm considers the high connectivity and the potentiality of propagating accurate appreciations so as to detect reliable leaders through these networks. This approach is evaluated in terms of precision using a real usage dataset. The results of the experimentation show the interest of our approach to detect TopN behavioral leaders that predict accurately the preferences of the other users. Besides, our approach can be harnessed in different application areas caring about the role of leaders.
Url:
DOI: 10.1109/ASONAM.2010.72
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 001C07
- to stream Hal, to step Curation: 001C07
- to stream Hal, to step Checkpoint: 002392
- to stream Main, to step Merge: 002C05
- to stream Main, to step Curation: 002B40
Le document en format XML
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status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName><orgName type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1109/ASONAM.2010.72</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>behavioral leaders</term><term>behavioral networks</term><term>navigational patterns</term><term>preference propagation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The development of the Web engendered the emergence of virtual communities. Analyzing information flows and discovering leaders through these communities becomes thus, a major challenge in different application areas. In this paper, we present an algorithm that aims at detecting leaders in the context of behavioral networks. This algorithm considers the high connectivity and the potentiality of propagating accurate appreciations so as to detect reliable leaders through these networks. This approach is evaluated in terms of precision using a real usage dataset. The results of the experimentation show the interest of our approach to detect TopN behavioral leaders that predict accurately the preferences of the other users. Besides, our approach can be harnessed in different application areas caring about the role of leaders.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Esslimani, Ilham" sort="Esslimani, Ilham" uniqKey="Esslimani I" first="Ilham" last="Esslimani">Ilham Esslimani</name></region><name sortKey="Boyer, Anne" sort="Boyer, Anne" uniqKey="Boyer A" first="Anne" last="Boyer">Anne Boyer</name><name sortKey="Brun, Armelle" sort="Brun, Armelle" uniqKey="Brun A" first="Armelle" last="Brun">Armelle Brun</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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