Rethinking information propagation: an attention-based perspective

Information propagation in Online Social Networks (OSNs) is commonly modeled as a diffusion process based on fixed interaction rules. However, such models are limited in their ability to capture adaptive and context-dependent interactions. In this work, we propose an attention-based formulation of information propagation in which influence emerges from dynamic interactions between node representations. Rather than following predefined transmission rules, propagation is governed by compatibility between node states and evolves over time. Experiments on synthetic networks show that the proposed model exhibits non-monotonic activation dynamics, in contrast to classical cascade processes. A key result is the emergence of distinct propagation regimes controlled by a structural parameter, which governs a transition from globally distributed, high-entropy interactions to localized, low-entropy dynamics. These findings indicate that propagation can be understood as a process of dynamic attention redistribution rather than cumulative transmission. As a result, the proposed approach provides a unified and flexible approach to modeling information propagation, generalizing classical diffusion models and capturing a broader range of behaviors.