Structure-based control of complex networks with nonlinear dynamics

Significance Many biological, technological, and social systems can be encoded as networks over which nonlinear dynamical processes such as cell signaling, information transmission, or opinion spreading take place. Despite many advances in network science, we do not know to what extent the network architecture shapes our ability to control these nonlinear systems. Here we extend a recently developed control framework that addresses this question and apply it to real networks of diverse types. Our results highlight the crucial role of a network’s feedback structure in determining robust control strategies, provide a dynamic-detail-independent benchmark for other control methods, and open up a promising research direction in the control of complex networks with nonlinear dynamics. What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.