Centrality measures and the role of non-normality for network control energy reduction
Provides a principled method for reducing control energy in network systems, relevant for control theory and network science.
The paper proposes Gramian-based centrality measures for driver node selection in complex networks to balance average and worst-case control energy, leveraging network non-normality linked to balanced realization.
Combinations of Gramian-based centrality measures are used for driver node selection in complex networks in order to simultaneously take into account conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction. The selection strategies that we propose are based on a characterization of the network non-normality, a concept we show is related to the idea of balanced realization.