Learning Information Spread in Content Networks

TL;DR

This paper proposes a continuous diffusion model for predicting content spread on social media, projecting nodes onto a latent space that captures temporal diffusion patterns, and demonstrates preliminary predictive results.

Contribution

It introduces a novel continuous diffusion model with latent space projection for social media content spread prediction, differing from traditional graph-based models.

Findings

Preliminary results show promising predictive accuracy.

Latent space proximity reflects temporal diffusion.

Model applied successfully on multiple datasets.

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.