Data-driven design of complex network structures to promote synchronization

We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization. When traditional optimization methods are inapplicable, due to uncertain or unknown node dynamics, we propose a data-driven approach leveraging datasets of relevant examples. We analyze two case studies, with linear and nonlinear node dynamics. First, we show how including node dynamics in the objective function makes the optimal graphs heterogeneous. Then, we compare various design strategies, finding the best either use data samples close to a specific Pareto front or combine a neural network and a genetic algorithm, performing statistically better than the best examples in the datasets.