Learning Information Spread in Content Networks

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.
Type de document :
Poster
ICLR 2014 - International Conference on Learning Representations, Apr 2014, Banff, Canada. pp.abs/1312.6169, 2014, CoRR
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https://hal.sorbonne-universite.fr/hal-01357961
Contributeur : Sylvain Lamprier <>
Soumis le : mardi 30 août 2016 - 16:41:35
Dernière modification le : samedi 15 décembre 2018 - 01:49:39

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  • HAL Id : hal-01357961, version 1

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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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