Incremental Learning in Social Networks to find Community Structure [ressource textuelle, sauf manuscrits] / Hussein Ibrahim Ahmed ABUZER (2025) / TDOC004

Public ISBD Titre : Incremental Learning in Social Networks to find Community Structure [ressource textuelle, sauf manuscrits] Type de document : document électronique Auteurs : Hussein Ibrahim Ahmed ABUZER, Auteur ; Mohamed Ahmed (Directeur de thèse) BOUDREF, Auteur Editeur : bouira [Algérie] : université akli mohand oulhadj Année de publication : 2025 ISBN/ISSN/EAN : TDOC004 Langues : Français (fre) Index. décimale : 004 Informatique Résumé : Complex network are one from widespread in the real world and exist in many filed Among complex networks as complex networks we find social network which are very used today. These networks have some topological features like degree distribution, high clustering coefficients..etc. One of subjects important in Complex network are Why finding and discovering of hidden community structure or clustering based on observed topological information One of the major problem are detection of communities in networks this problem called NP-difficult like size of network ..etc Many approaches have been proposed in literature in the case of static social networks and some few works have focused on dynamic social network whose structure evolves over time. In this thesis, we propose to develop new method based on incremental learning techniques for analysis and inference in social networks Incremental Learning in Social Networks to find Community Structure [ressource textuelle, sauf manuscrits] [document électronique] / Hussein Ibrahim Ahmed ABUZER, Auteur ; Mohamed Ahmed (Directeur de thèse) BOUDREF, Auteur . - bouira [Algérie] : université akli mohand oulhadj, 2025. ISSN : TDOC004 Langues : Français (fre) Index. décimale : 004 Informatique Résumé : Complex network are one from widespread in the real world and exist in many filed Among complex networks as complex networks we find social network which are very used today. These networks have some topological features like degree distribution, high clustering coefficients..etc. One of subjects important in Complex network are Why finding and discovering of hidden community structure or clustering based on observed topological information One of the major problem are detection of communities in networks this problem called NP-difficult like size of network ..etc Many approaches have been proposed in literature in the case of static social networks and some few works have focused on dynamic social network whose structure evolves over time. In this thesis, we propose to develop new method based on incremental learning techniques for analysis and inference in social networks

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Code-barres	Date d'acquisition	Origine	Cote	Support	Localisation	Section	Disponibilité
TDOC004	004 ABU1	Livre	BIB-SE	BIB-SE	Disponible	07/07/2025

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