A Dynamic Heterogeneous Graph Perception Network with Time-Based Mini-Batch for Information Diffusion Prediction

Wei Fan, Meng Liu, Yong Liu

Abstract: Information diffusion prediction is an important task to understand how information spreads among users. Most previous studies either only focused on the use of diffusion sequences, or only used social networks between users to make prediction, but such modeling is not sufficient to model the diffusion process. In this paper, we propose a novel Dynamic Heterogeneous Graph Perception Network with Time-Based Mini-Batch (DHGPNTM) that can combine dynamic diffusion graph and social graph for information diffusion prediction. First, we propose a Graph Perception Network (GPN) to learn user embedding in dynamic heterogeneous graphs, and combine temporal information with user embedding to capture users’ dynamic preferences. Then we use a multi-head attention to generate users’ context-dependence embedding, and design a fusion gate to selectively integrate users’ dynamic preferences and context-dependence embedding. The extensive experiments on real datasets demonstrate the effectiveness and efficiency of our model.