A Deep Prediction Framework for Multi-Source Information Via Heterogeneous GNN

Predicting information diffusion is a fundamental task in online social networks (OSNs). Recent studies mainly focus on the popularity prediction of specific content but ignore the correlation between multiple pieces of information. The topic is often used to correlate such information and can correspond to multi-source information. The popularity of a topic relies not only on information diffusion time but also on users' followership. Current solutions concentrate on hard time partition, lacking versatility. Meanwhile, the hop-based sampling adopted in state-of-the-art (SOTA) methods encounters redundant user followership. Moreover, many SOTA methods are not designed with good modularity and lack evaluation for each functional module and enlightening discussion. This paper presents a novel extensible framework, coined as HIF, for effective popularity prediction in OSNs with four original contributions. First, HIF adopts a soft partition of users and time intervals to better learn users' behavioral preferences over time. Second, HIF utilizes weighted sampling to optimize the construction of heterogeneous graphs and reduce redundancy. Furthermore, HIF supports multi-task collaborative optimization to improve its learning capability. Finally, as an extensible framework, HIF provides generic module slots to combine different submodules (e.g., RNNs, Transformer encoders). Experiments show that HIF significantly improves performance and interpretability compared to SOTAs.