DANCer: Dynamic Attributed Network with Community Structure Generator

Abstract : We propose a new generator for dynamic attributed networks with community structure which follow the known properties of real-world networks such as preferential attachment, small world and ho-mophily. After the generation, the different graphs forming the dynamic network as well as its evolution can be displayed in the interface. Several measures are also computed to evaluate the properties verified by each graph. Finally, the generated dynamic network, the parameters and the measures can be saved as a collection of files.
Type de document :
Communication dans un congrès
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2016, Riva del Garda, Italy. 9853, pp.41 - 44, 2016, Machine Learning and Knowledge Discovery in Databases. 〈10.1007/978-3-319-46131-1_9〉
Liste complète des métadonnées

https://hal-auf.archives-ouvertes.fr/hal-01377321
Contributeur : Baptiste Jeudy <>
Soumis le : jeudi 6 octobre 2016 - 17:32:59
Dernière modification le : jeudi 26 juillet 2018 - 01:10:44
Document(s) archivé(s) le : vendredi 3 février 2017 - 19:00:21

Fichier

DANCER_cr2.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Oualid Benyahia, Christine Largeron, Baptiste Jeudy, Osmar Zaïane. DANCer: Dynamic Attributed Network with Community Structure Generator. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2016, Riva del Garda, Italy. 9853, pp.41 - 44, 2016, Machine Learning and Knowledge Discovery in Databases. 〈10.1007/978-3-319-46131-1_9〉. 〈hal-01377321〉

Partager

Métriques

Consultations de la notice

208

Téléchargements de fichiers

340