IMF-Cas: A Multiscale Framework for Information Cascade Prediction via Behavioral Imitation and Dynamic Graph Modeling

Information cascade prediction has demonstrated wide application value in public opinion guidance and social diffusion modeling. However, existing approaches suffer from limited capabilities in modeling long-range temporal dependencies, lack unified temporal representations that account for varying user activity patterns, and often overlook the imitation-driven motivations underlying user diffusion behavior. To address these limitations, we propose imitation-based multiscale framework for cascade prediction (IMF-Cas), a cascade prediction framework integrating behavioral imitation with dynamic graph representation. The framework introduces a social time transformation to normalize natural time into an activity-aware timeline, mitigating bias from uneven user activity distributions. A sparse graph convolutional network (GCN) encodes local structural features at each time slice, while a transformer with sparse attention captures temporal dependencies across slices for efficient long-range sequence modeling. We design a multiscale imitation mechanism that characterizes users' reposting motivations through micro-level features (trust relationships and interaction intensity) and macro-level features (local imitation ratio and clustering coefficient). A dynamic information hotness function combining semantic properties with temporal decay is integrated to model the evolving attractiveness of content. Experiments on the Twitter and Weibo datasets show that IMF-Cas reduces mean squared logarithmic error (MSLE) by 35.3% and 22.9% relative to the strongest competing baseline on each dataset, with consistent gains on micro-level tasks, validating its effectiveness in capturing cascade dynamics. These results also point to practical cascade control strategies, including early warning systems and targeted interventions for rumor suppression.