Two Evidential Data Based Models for Influence Maximization in Twitter

Abstract : Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the " word of mouth " effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, the proposed influence estimation measure fuses many influence aspects in Twitter, like the importance of the user in the network structure and the popularity of user's tweets (messages). In our experiments, we compare the proposed solutions to existing ones and we show the performance of our models.
Type de document :
Article dans une revue
Knowledge-Based Systems, Elsevier, 2017, 〈10.1016/j.knosys.2017.01.014〉
Liste complète des métadonnées

Littérature citée [34 références]  Voir  Masquer  Télécharger
Contributeur : Arnaud Martin <>
Soumis le : dimanche 15 janvier 2017 - 15:29:10
Dernière modification le : jeudi 7 février 2019 - 16:59:09
Document(s) archivé(s) le : dimanche 16 avril 2017 - 12:24:18


Fichiers produits par l'(les) auteur(s)



Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Hend Ben Hadji, Boutheina Ben Yaghlane. Two Evidential Data Based Models for Influence Maximization in Twitter. Knowledge-Based Systems, Elsevier, 2017, 〈10.1016/j.knosys.2017.01.014〉. 〈hal-01435733〉



Consultations de la notice


Téléchargements de fichiers