DynaFLUX: Implicit Dynamics-Preserving Reinforcement Learning for Topology-Free Influence Maximization

Influence maximization (IM) aims to select a small set of seed nodes whose activation triggers a maximal cascade. Existing methods typically assume access to the network topology or a reliable surrogate, which is often unavailable in practice due to noisy, privacy-protected, or partially observed links. These settings pose two challenges: (1) the lack of explicit topology removes a key inductive constraint for diffusion modeling, undermining influence estimation, and (2) nonlinear, temporally dependent node interactions yield complex multivariate time series that hinder topology inference. We propose DynaFLUX, an end-to-end generative framework for IM under hidden topology. DynaFLUX learns a compact surrogate of latent dynamics directly from observed time series, and jointly optimizes a seed-selection policy via reinforcement learning. A self-attention pointer network captures long-range dependencies for seed generation, while an influence-prediction module infers a surrogate topology and uses Monte Carlo diffusion to provide policy-gradient rewards. Experiments show that DynaFLUX accurately identifies influential spreaders and consistently outperforms state-of-the-art baselines in unseen topology scenarios.