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Poster communications

Learning Information Spread in Content Networks

Cédric Lagnier 1 Simon Bourigault 2 Sylvain Lamprier 2 Ludovic Denoyer 2 Patrick Gallinari 2
1 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble
2 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We introduce a model for predicting the diffusion of content information on social media. When propagation is usually modeled on discrete graph structures, we introduce here a continuous diffusion model, where nodes in a diffusion cascade are projected onto a latent space with the property that their proximity in this space reflects the temporal diffusion process. We focus on the task of predicting contaminated users for an initial initial information source and provide preliminary results on differents datasets.
Document type :
Poster communications
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https://hal.sorbonne-universite.fr/hal-01357961
Contributor : Sylvain Lamprier <>
Submitted on : Tuesday, August 30, 2016 - 4:41:35 PM
Last modification on : Friday, January 8, 2021 - 5:34:10 PM

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  • HAL Id : hal-01357961, version 1

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UPMC | CNRS | LIP6 | SORBONNE-UNIVERSITE | LIG | SU-SCIENCES | UGA

Citation

Cédric Lagnier, Simon Bourigault, Sylvain Lamprier, Ludovic Denoyer, Patrick Gallinari. Learning Information Spread in Content Networks. ICLR 2014 - International Conference on Learning Representations, Apr 2014, Banff, Canada. pp.abs/1312.6169, 2014, CoRR. ⟨hal-01357961⟩

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