Egocentric storylines for visual analysis of large dynamic graphs

Abstract : Large dynamic graphs occur in many fields. While overviews are often used to provide summaries of the overall structure of the graph, they become less useful as data size increases. Often analysts want to focus on a specific part of the data according to domain knowledge, which is best suited by a bottom-up approach. This paper presents an egocentric, bottom-up method to exploring a large dynamic network using a storyline representation to summarise localized behavior of the network over time.
Type de document :
Communication dans un congrès
Big Data: International Conference on Big Data, Oct 2013, Santa Clara, United States. Big Data, 2013 IEEE International Conference on, pp.56-62, 2013, 〈10.1109/BigData.2013.6691715〉
Liste complète des métadonnées

Littérature citée [38 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01275387
Contributeur : Arnaud Sallaberry <>
Soumis le : mercredi 17 février 2016 - 13:44:57
Dernière modification le : jeudi 24 mai 2018 - 15:59:25
Document(s) archivé(s) le : mercredi 18 mai 2016 - 13:12:02

Fichier

muelder_al_2013.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Chris Muelder, Tarik Crnovrsanin, Arnaud Sallaberry, Kwan-Liu Ma. Egocentric storylines for visual analysis of large dynamic graphs. Big Data: International Conference on Big Data, Oct 2013, Santa Clara, United States. Big Data, 2013 IEEE International Conference on, pp.56-62, 2013, 〈10.1109/BigData.2013.6691715〉. 〈lirmm-01275387〉

Partager

Métriques

Consultations de la notice

60

Téléchargements de fichiers

158