PhD graduated
Team : MLIA
Departure date : 11/10/2016

Supervision : Patrick GALLINARI

Co-supervision : LAMPRIER Sylvain

Apprentissage de la dynamique de propagation d'info dans les réseaux sociaux

In this thesis, we study information diffusion in online social networks. Websites like Facebook or Twitter have indeed become information medias, on which users create and share a lot of data. Most existing models of the information diffusion phenomenon relies on strong hypothesis about the structure and dynamics of diffusion. In this document, we study the problem of diffusion prediction in the context where the social graph is unknown and only user actions are observed.

  • We propose a learning algorithm for the independant cascades model that does not take time into account. Experimental results show that this approach obtains better results than time-based learning schemes.
  • We then propose several representations learning methods for this task of diffusion prediction. This let us define more compact and faster models.
  • Finally, we apply our representation learning approach to the source detection task, where it obtains much better results than graph-based approaches.

Defence : 11/10/2016 - 14h - Site Jussieu 25-26/105

Jury members :

M. Fabrice Rossi, Paris 1 Panthéon Sorbonne [Rapporteur]
M. Julien Velcin, Université Lumière Lyon 2 [Rapporteur]
Mme. Christine Largeron, Université Jean Monnet
M. Christophe Marsala, Université Pierre et Marie Curie
M. Sylvain Lamprier, Université Pierre et Marie Curie
M. Patrick Gallinari , Université Pierre et Marie Curie

2014-2017 Publications