  Information diffusion prediction is a fundamental task for understanding the
information propagation process. It has wide applications in such as
misinformation spreading prediction and malicious account detection. Previous
works either concentrate on utilizing the context of a single diffusion
sequence or using the social network among users for information diffusion
prediction. However, the diffusion paths of different messages naturally
constitute a dynamic diffusion graph. For one thing, previous works cannot
jointly utilize both the social network and diffusion graph for prediction,
which is insufficient to model the complexity of the diffusion process and
results in unsatisfactory prediction performance. For another, they cannot
learn users' dynamic preferences. Intuitively, users' preferences are changing
as time goes on and users' personal preference determines whether the user will
repost the information. Thus, it is beneficial to consider users' dynamic
preferences in information diffusion prediction.
  In this paper, we propose a novel dynamic heterogeneous graph convolutional
network (DyHGCN) to jointly learn the structural characteristics of the social
graph and dynamic diffusion graph. Then, we encode the temporal information
into the heterogeneous graph to learn the users' dynamic preferences. Finally,
we apply multi-head attention to capture the context-dependency of the current
diffusion path to facilitate the information diffusion prediction task.
Experimental results show that DyHGCN significantly outperforms the
state-of-the-art models on three public datasets, which shows the effectiveness
of the proposed model.
