Learning Information Spread in Content Networks
Machine Learning
2014-02-04 v2 Social and Information Networks
Physics and Society
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
@article{arxiv.1312.6169,
title = {Learning Information Spread in Content Networks},
author = {Cédric Lagnier and Simon Bourigault and Sylvain Lamprier and Ludovic Denoyer and Patrick Gallinari},
journal= {arXiv preprint arXiv:1312.6169},
year = {2014}
}
Comments
4 pages