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.
Cite
Text
Lagnier et al. "Learning Information Spread in Content Networks." International Conference on Learning Representations, 2014.Markdown
[Lagnier et al. "Learning Information Spread in Content Networks." International Conference on Learning Representations, 2014.](https://mlanthology.org/iclr/2014/lagnier2014iclr-learning/)BibTeX
@inproceedings{lagnier2014iclr-learning,
title = {{Learning Information Spread in Content Networks}},
author = {Lagnier, Cédric and Bourigault, Simon and Lamprier, Sylvain and Denoyer, Ludovic and Gallinari, Patrick},
booktitle = {International Conference on Learning Representations},
year = {2014},
url = {https://mlanthology.org/iclr/2014/lagnier2014iclr-learning/}
}