Machine learning for link prediction in complex networks / Messaoud Babaghayou

Public ISBD Titre : Machine learning for link prediction in complex networks Type de document : texte manuscrit Auteurs : Messaoud Babaghayou, Auteur ; Abdallah Lakhdari, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2016 Importance : 79 p. Format : 30 cm. Accompagnement : 1 disque optique numérique Note générale : Option : Networks, systems and distributed applications ( Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Machine Learning  Link Prediction  Complex Networks  Supervised Leaning  Unsupervised Learning  Classification  Node-based Metrics Résumé : Nowdays, networks are omnipresent. The study and understanding of these networks become a greater need. The purpose of this work, is to investigate link prediction task in complex networks using Machine learning techniques. In fact, we propose two approaches to perform link prediction: supervised and unsupervised one. In both techniques a link or a pair of nodes is characterized by several features based on network topology-based metrics. In addition, we investigate many combined features. Concerning the supervised approach, we investigate the KNN and decision tree methods to build the link prediction models. While in the unsupervised approach, we rely on ranking strategy. An experimental study is performed on real networks. The results show that the supervised approach using gathered features reaches good performances with 84% f-measure. note de thèses : Mémoire de master en informatique Machine learning for link prediction in complex networks [texte manuscrit] / Messaoud Babaghayou, Auteur ; Abdallah Lakhdari, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2016 . - 79 p. ; 30 cm. + 1 disque optique numérique. Option : Networks, systems and distributed applications ( Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Machine Learning  Link Prediction  Complex Networks  Supervised Leaning  Unsupervised Learning  Classification  Node-based Metrics Résumé : Nowdays, networks are omnipresent. The study and understanding of these networks become a greater need. The purpose of this work, is to investigate link prediction task in complex networks using Machine learning techniques. In fact, we propose two approaches to perform link prediction: supervised and unsupervised one. In both techniques a link or a pair of nodes is characterized by several features based on network topology-based metrics. In addition, we investigate many combined features. Concerning the supervised approach, we investigate the KNN and decision tree methods to build the link prediction models. While in the unsupervised approach, we rely on ranking strategy. An experimental study is performed on real networks. The results show that the supervised approach using gathered features reaches good performances with 84% f-measure. note de thèses : Mémoire de master en informatique

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