Modeling Continuous-Time Dynamics of Cascades for Accurate Information Popularity Prediction
Accurately predicting the future popularity of information cascades is crucial for various applications, but existing methods often oversimplify the continuous-time dynamics of the underlying diffusion process. This work proposes ConCat, a model that leverages neural Ordinary Differential Equations and neural Temporal Point Processes to effectively capture both the continuous-time dynamics and the global trend of information cascades, leading to superior performance in popularity prediction.
Predicting the Future Popularity of Information Cascades Using Dynamic Cues-Driven Diffusion Models
CasFT leverages observed information cascades and dynamic cues extracted via neural ODEs to guide the generation of future popularity-increasing trends through a diffusion model, which are then combined with the spatiotemporal patterns in the observed information cascade to make the final popularity prediction.
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