MH-Diff: Muti-scale Hyperbolic Model for Information Diffusion Prediction

Information diffusion prediction aims to predict which users are more likely to engage in the diffusion path on the next timestamp. Previous methods focus on either capturing dynamic dependencies in Euclidean space or static implicit dependencies in hyperbolic space, lacking the double consideration of both the implicit hierarchical relationships that are hard captured in Euclidean space and dynamic user preferences over time. In this study, we propose a novel Multi-scale Hyperbolic Diffusion model (MH-Diff), which leverages the unique properties of hyperbolic space to capture the complex hierarchical relationships in both social graphs and diffusion cascades. Specifically, we utilize the hyperbolic space to represent user dependencies and implicit tree-like structures, which are difficult to model in traditional Euclidean space. Next, we introduce a dynamic hyperbolic learning module with multi-scale sequential hypergraph attention network to captures users' dynamic preferences at different time scales. Moreover, we leverage a contextual prediction module to enhance the interaction among user hyperbolic representations in the current cascade. Finally, we evaluate MH-Diff on three real-world datasets and demonstrate that it significantly outperforms state-of-the-art models, validating the superior capability of hyperbolic space in capturing both global statics and temporal dynamics.