DANCer: Dynamic Attributed Network with Community Structure Generator

Oualid Benyahia (1) , Christine Largeron (1) , Baptiste Jeudy (1) , Osmar R Zaïane (2)

1 LHC - Laboratoire Hubert Curien
2 University of Alberta

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.

Mots clés: Social network mining; Attributed graph; Synthetic data generator

Domaines: Réseaux sociaux et d'information [cs.SI]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Modélisation et simulation

Oualid Benyahia, Christine Largeron, Baptiste Jeudy, Osmar R 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. pp.41 - 44, ⟨10.1007/978-3-319-46131-1_9⟩. ⟨hal-01377321⟩