Dimensionality reduction of complex dynamical systems

One of the outstanding problems in complexity science and engineering is the study of high-dimensional networked systems and of their susceptibility to transitions to undesired states as a result of changes in external drivers or in the structural properties. Because of the incredibly large number of parameters controlling the state of such complex systems and the heterogeneity of its components, the study of their dynamics is extremely difficult. Here we propose an analytical framework for collapsing complex N-dimensional networked systems into an S+1-dimensional manifold as a function of S effective control parameters with S << N. We test our approach on a variety of real-world complex problems showing how this new framework can approximate the system's response to changes and correctly identify the regions in the parameter space corresponding to the system's transitions. Our work offers an analytical method to evaluate optimal strategies in the design or management of networked systems.

Automated summary

One of the main challenges in complexity science and engineering is understanding high-dimensional networked systems and their susceptibility to transitions to undesired states. The authors propose an analytical framework to collapse complex N-dimensional networked systems into a lower-dimensional manifold, which can simplify the study of system dynamics and help in identifying optimal strategies in the design or management of networked systems.