Learning Information Spread in Content Networks
This work addresses the challenge of modeling information spread for social media analysis, but it appears incremental as it builds on existing diffusion models with a continuous approach.
The authors tackled the problem of predicting information diffusion on social media by introducing a continuous diffusion model that projects nodes into a latent space based on temporal diffusion, and they provided preliminary results on various datasets.
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.