Recent research across complex social networks and neuronal dynamics reveals a multifaceted exploration into emergent behaviors and structural dynamics. In the study of social networks, researchers increasingly utilize advanced network science and computational modeling to analyze dynamic interactions, structural formations, and resilient properties amidst various disruptions. Concurrently, in neuroscience, the study of neuronal dynamics— particularly oscillatory behaviors in neuronal dynamics, analogous to patterns seen in social networks following external stimuli- poses significant theoretical and practical challenges. The self-contained oscillatory patterns and bifurcation phenomena in neuron potential underscore the necessity for detailed nonlinear analysis through numerical integrations and computer simulations across diverse parameter spaces. These simulations explore the concept of structural target control, focusing on strategies to manipulate critical nodes within social networks while considering their inherent structural characteristics. Such simulations provide crucial insights into system stability, phase space dynamics, and bifurcation diagrams within foundational models, such as the Fitzhugh-Nagumo (FN) and Hodgkin-Huxley (HH) models, as well as in more advanced interdisciplinary models. These simulations incorporate methodologies from network science and control theory, crucial for identifying minimal sets of influential nodes, or "drivers," capable of steering the dynamics of targeted social network segments. It sets a foundation for future studies that integrate advanced computational techniques with empirical data to address emerging challenges in understanding and managing complex networks.
Nikaj et al. (Fri,) studied this question.