  Information diffusion prediction is a fundamental task which forecasts how an
information item will spread among users. In recent years, deep learning based
methods, especially those based on recurrent neural networks (RNNs), have
achieved promising results on this task by treating infected users as
sequential data. However, existing methods represent all previously infected
users by a single vector and could fail to encode all necessary information for
future predictions due to the mode collapse problem. To address this problem,
we propose to employ the idea of disentangled representation learning, which
aims to extract multiple latent factors representing different aspects of the
data, for modeling the information diffusion process. Specifically, we employ a
sequential attention module and a disentangled attention module to better
aggregate the history information and disentangle the latent factors.
Experimental results on three real-world datasets show that the proposed model
SIDDA significantly outperforms state-of-the-art baseline methods by up to 14%
in terms of hits@N metric, which demonstrates the effectiveness of our method.
