Dynamic Mitigation of Cascading Contagion in Complex Networks via High-Frequency Parametric Renormalization

This work presents a theoretical framework for dynamically mitigating cascading contagion in complex networks using high-frequency temporal modulation. Within a continuous-time SIS model, we introduce a fast, zero-mean oscillatory modulation of interaction weights and show, using multiscale averaging, that the effective adjacency matrix is renormalized via a Bessel function dependence. This dynamic renormalization reduces the effective spectral radius of the network, leading to a suppression of the macroscopic spreading rate and a shift of the epidemic threshold. The model predicts a crossover from systemic cascade behavior to a localized spreading regime as the forcing amplitude approaches a critical threshold. This approach provides a non-destructive alternative to static network interventions, enabling dynamic control of systemic risk while preserving structural connectivity.