Predicting the future popularity of information in online social networks is a crucial yet challenging task, due to the complex spatiotemporal dynamics underlying information diffusion. Existing methods typically use structural or sequential patterns within the observation window as direct inputs for subsequent popularity prediction. However, most approaches lack the ability to explicitly model the overall trend of popularity up to the prediction time, which leads to limited predictive capability. To address these limitations, we propose VNOIP, a novel method based on variational neural Ordinary Differential Equations (ODEs) for information popularity prediction. Specifically, VNOIP introduces bidirectional jump ODEs with attention mechanisms to capture long-range dependencies and bidirectional context within cascade sequences. Furthermore, by jointly considering both cascade patterns and overall trend temporal patterns, VNOIP explicitly models the continuous-time dynamics of popularity trend trajectories with variational neural ODEs. Additionally, a knowledge distillation loss is employed to align the evolution of prior and posterior latent variables. Extensive experiments on real-world datasets demonstrate that VNOIP is highly competitive in both prediction accuracy and efficiency compared to state-of-the-art baselines.

With the rise of social media, accurately predicting information diffusion has become crucial for a wide range of applications. Existing methods usually employ sequential hypergraphs to model users' latent interaction preferences and use self-attention mechanisms to capture dependencies with users. However, they typically focus on a single temporal scale and lack the ability to effectively model temporal influence, which limits their performance in diffusion prediction tasks. To address these limitations, we propose a novel method (MHPS) to enhance information diffusion prediction via multiple granularity hypergraphs and a position-aware sequence model. Specifically, MHPS constructs hypergraph sequences of different granularities by grouping user interactions according to various time intervals. Additionally, to further enhance the modeling of temporal influence, two types of cross-attention mechanisms, namely next-step positional cross-attention and source influence cross-attention, are introduced within the cascade representation. The next-step positional cross-attention captures target position awareness, while the source influence cross-attention focuses on the impact of the initial source. Then, gating mechanisms and GRUs are employed to fuse the different attention outputs and predict the next target user. Extensive experiments on real-world datasets demonstrate that MHPS achieves competitive performance against state-of-the-art methods. The average improvements are up to 7.82% in terms of Hits@10 and 5.60% in terms of MAP@100. Our code is available at https://github.com/cgao-comp/MHPS.