  This paper describes a novel diffusion model, DyDiff-VAE, for information
diffusion prediction on social media. Given the initial content and a sequence
of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for
other potential users and predict the corresponding user rankings. Inferring
user interests from diffusion data lies the foundation of diffusion prediction,
because users often forward the information in which they are interested or the
information from those who share similar interests. Their interests also evolve
over time as the result of the dynamic social influence from neighbors and the
time-sensitive information gained inside/outside the social media. Existing
works fail to model users' intrinsic interests from the diffusion data and
assume user interests remain static along the time. DyDiff-VAE advances the
state of the art in two directions: (i) We propose a dynamic encoder to infer
the evolution of user interests from observed diffusion data. (ii) We propose a
dual attentive decoder to estimate the propagation likelihood by integrating
information from both the initial cascade content and the forwarding user
sequence. Extensive experiments on four real-world datasets from Twitter and
Youtube demonstrate the advantages of the proposed model; we show that it
achieves 43.3% relative gains over the best baseline on average. Moreover, it
has the lowest run-time compared with recurrent neural network based models.
