Learning Distributed Representations of Users for Source Detection in Online Social Networks

  • Simon BourigaultEmail author
  • Sylvain Lamprier
  • Patrick Gallinari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)


In this paper, we study the problem of source detection in the context of information diffusion through online social networks. We propose a representation learning approach that leads to a robust model able to deal with the sparsity of the data. From learned continuous projections of the users, our approach is able to efficiently predict the source of any newly observed diffusion episode. Our model does not rely neither on a known diffusion graph nor on a hypothetical probabilistic diffusion law, but directly infers the source from diffusion episodes. It is also less complex than alternative state of the art models. It showed good performances on artificial and real-world datasets, compared with various state of the art baselines.


Latent Space Online Social Network Source User Infected Node Artificial Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by the following projects: Xu Guangqi 2016 Deep learning for Large Scale Dynamic and Spatio-Temporal Data; REQUEST Investissement d’Avenir 2014 and LOCUST ANR 2015 (ANR-15-CE23-0027-01).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Simon Bourigault
    • 1
    Email author
  • Sylvain Lamprier
    • 1
  • Patrick Gallinari
    • 1
  1. 1.Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6ParisFrance

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